Long-Term Dynamics of Mangrove Vegetation Coverages in Indonesia’s National Parks Derived from Remote Sensing Data

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Rahman As-syakur, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7554686/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Mangroves are coastal ecosystems essential for coastline protection, biodiversity preservation, and carbon sequestration and storage. A primary strategy for mangrove preservation in Indonesia is the establishment of protected zones, including National Parks. Monitoring mangrove ecosystems over extensive spatial areas necessitates sophisticated methodologies, including the utilisation of satellite-based remote sensing data. This study examines the dynamics of mangrove cover change in all National Parks in Indonesia utilising the Enhanced Vegetation Index (EVI) obtained from Remote Sensing Imagery from 1995 to 2024. We used the Landsat Imagery from Landsat 5 TM (1995 to 2000), Landsat 7 ETM+ (2000 to 2012), and Landsat 8 OLI-TIRS (2013 to 2024). The analysis includes 23 National Parks featuring coastal mangrove ecosystems. Lorentz (LRZ) and Sembilang (SBG) National Parks encompass the most extensive mangrove regions, predominantly situated in significant river estuaries that offer optimal conditions for mangrove proliferation. Mangrove ecosystems in National Parks located on larger islands generally display elevated temporal mean EVI values relative to those on smaller islands. Significant reductions in EVI were noted in Ujung Kulon (UJK), Komodo (KMD), Kutai (KTI), and Way Kambas (WKB) National Parks, while Alas Purwo (APW) exhibited the least loss, indicating comparatively well-preserved mangrove conditions. Trend analysis indicated that over 80% of mangrove regions within each National Park displayed favourable EVI trends, signifying enhancements in mangrove ecological conditions. In contrast, localised regions exhibiting negative trends were predominantly linked to heavy tourist and land conversion operations, as noted in KMD and KTI. Conversely, National Parks situated in more isolated areas, like APW, exhibited constant positive EVI trends, correlating with substantial mangrove area expansion. The mangrove ecosystem cover within National Parks is generally still well preserved, with no extreme changes observed. Several factors, such as tourism activities, forest fires, and land-use conversion, are the main drivers of mangrove cover decline in certain locations. The results of this study are expected to serve as a reference for future mangrove ecosystem management, particularly within the framework of National Park–based conservation. Mangrove National Parks Enhanced Vegetation Index (EVI) Satellite Imagery Remote Sensing Data Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Mangroves areas is category of coastal vegetation that play crucial role on climate change mitigation, disaster resilience, and preserving coastal biodiversity (Hamza et al., 2022 ; Macintosh et al., 2002 ; Segaran et al., 2023 ). Mangrove ecosystems hold significant ecological and economic importance in coastal areas. They serve crucial roles as habitats for a wide variety of marine organisms, such as fish and crustaceans, providing essential nourishment and protection from the conditions of the open ocean (Carrasquilla-Henao et al., 2022 ; Nozarpour et al., 2023 ). As an estuarine ecosystem, mangroves serve as the terminal point for terrestrial runoff and riverine discharge, resulting in the accumulation of both organic and inorganic materials transported from the land (Jennerjahn & Ittekkot, 2002 ). This leads to exceptionally high productivity within the mangrove ecosystem, enabling it to supply food for a wide variety of organisms that inhabit it (Nagelkerken et al., 2008 ). In the context of climate change, mangrove ecosystems function as highly effective carbon sinks, exhibiting superior carbon storage and sequestration capacity compared to other tropical forest types (M. F. Adame et al., 2021 ). Mangroves also have function in protecting coastal areas from various climate change-related threats, such as sea level rise, strong winds, and storms (Van Hespen et al., 2023 ; Zhang et al., 2022 ). From an economic perspective, mangrove ecosystems support the livelihoods of coastal communities by providing resources such as harvestable marine species, timber, and opportunities for ecotourism (Blanton et al., 2024 ). As a tropical region, Indonesia provides a suitable habitat for mangroves, particularly in areas with large river estuaries (Bhowmik et al., 2022 ; Moschetto et al., 2021 ; Wang et al., 2021 ). According to the National Mangrove Map from 2021 (Ditjen PDASRH, 2021 ), Indonesia boast a total mangrove forest area of 3.4 million hectares. The distribution of mangrove ecosystem spans from the western to eastern region including the eastern coast of Sumatera, Kalimantan, the Northen Coast of Java, Sulawesi, and the western coast of Papua. These areas are primarily located in estuarine environments formed by major river systems in Indonesia. Indonesia’s mangrove ecosystems are distributed across various biogeographic regions and ecological settings. On the larger islands characterized predominantly as lowland mangrove areas are typically concentrated in expansive river estuaries, where fine sediment accumulation supports the development of dense and extensive mangrove stands (Cinco-Castro et al., 2022 ; Walsh & Nittrouer, 2004 ). These systems are particularly prevalent in western Indonesia, including Sumatra, Java, and Kalimantan (Kusmana, 2013 ; Rahman et al., 2024). Nevertheless, substantial lowland mangrove areas are also present along the southern coastal fringes of Papua (Setyadi et al., 2021 ). In contrast, mangrove assemblages in smaller islands and archipelagos often establish on karstic substrates and are directly exposed to open ocean conditions. These settings are generally associated with lower precipitation regimes, often classified under semi-arid climatic zones (M. Adame et al., 2021 ; Thivakaran et al., 2020 ). Such ecological differences lead to notable variation in mangrove structure, density, and species composition when compared to the more expansive lowland or estuarine mangrove systems (Dunham, 2014 ; Torres et al., 2023 ). Mangrove ecosystems in coastal regions face significant pressure from environmental factors and human activities (Gitau et al., 2023 ; Maina et al., 2021 ). These vital ecosystems face threats from human activities like deforestation, pollution, and land reclamation for farming and urban growth (Bhowmik et al., 2022 ; Moschetto et al., 2021 ; Wang et al., 2021 ). The expansion of aquaculture, particularly shrimp farming, has resulted in a significant loss of mangrove cover (Akber et al., 2020 ; Herbeck et al., 2020 ). More than 62% of mangrove loss caused by human activities has occurred over the past two decades (Goldberg et al., 2020 ). In addition, other drivers of mangrove ecosystem degradation have been exacerbated by climate change, including sea level rise, extreme weather events, and increasing of the global sea surface temperature (Alongi, 2015 ; Friess et al., 2022 ; Lovelock et al., 2015 ; Ward et al., 2016 ). These phenomena pose significant threats to the health and distribution of mangrove ecosystems (Chung et al., 2023 ; Ward et al., 2016 ). The degradation of mangrove forest not only harms biodiversity but also impacts communities relying on mangroves for fish, timber, Preserving and coastal protection (Asari et al., 2021 ; Cahyaningsih et al., 2022 ; Carugati et al., 2018 ). Mangroves is crucial for ecological balance, marine life support, and climate change mitigation (Kumari & Rathore, 2021 ). Conservation strategies must address both human and natural threats to sustain these vital coastal areas (Arifanti et al., 2022 ; Hidayah et al., 2024 ). The presence of mangrove within conservation zones represents an effort to protect this ecosystems from threats and degradation. The rationale behind safeguarding these estuarine habitats within conservation zones stems from their immense ecological value and the critical ecosystem services they provide. Mangrove are renowned for their role as nurseries for a diverse array of marine life, serving as natural barriers against coastal erosion and storm surges, and acting as significant carbon sinks that mitigate climate changes impact (Arifanti et al., 2022 ; Murdiyarso et al., 2015 ; Uddin et al., 2023 ). Within Indonesia’s comprehensive network of protected areas, National Parks play a particularly crucial role in mangrove conservation. Based on mangrove coverages in Indonesia from National Mangrove Maps, over than 22.2% has been designated for conservation efforts, including national parks (Ditjen PDASRH, 2021 ). However, few National Parks explicitly designate mangrove ecosystems as their primary management focus. Only Sembilang and Lorentz National Parks have established mangroves as the central objective of their conservation and management efforts (Claudino-Sales, 2018 ; Silvius et al., 2018 ). While other National Park mostly coverages by Low-Highland Forest that some of these have the coastal areas and have mangrove ecosystem. Most other National Parks in Indonesia are predominantly terrestrial forest ecosystems, some of which include coastal zones with mangrove areas. As a form of conservation area scheme, National Parks play a critical role in protecting mangrove cover. Ideally, these park could serve as role models for mangrove conservation efforts whether managed by governmental institutions or through collaborative engagement with local communities. However, there remains a paucity of comprehensive research specifically addressing the dynamics of mangrove cover across all National Parks in Indonesia. The major challenges lies in the extensive distribution of mangrove areas, with some located in remote and inaccessible regions. Given the spatial extent and dispersal of mangrove ecosystems within National Parks, remote sensing approaches supported by cloud computing platform offer a promising solution for monitoring and analysis. Existing remote sensing based studies have typically focus on individual Parks, such as Sembilang (Purwanto et al., 2022 ), Alas Purwo, Lorentz (Setyadi et al., 2021 ), and Kutai (Budiarsa & Rizal, 2022 ; Najamuddin et al., 2024 ), rather than providing a holistic national scale assessment. This study will undertake a comprehensive examination of the dynamics of mangrove vegetation changes throughout all National Park regions in Indonesia using remote sensing data. The assessment of forest condition status can be carried out by analyzing Vegetation Index (VI) values based on Remote Sensing data, which provide insight of health, density, and overall state of forest ecosystems (Akbar et al., 2020 ; Arshad et al., 2020 ; Chellamani et al., 2014 ; Hidayah et al., 2024 ; Vidhya et al., 2014 ). This method involves observing the Earth Observation Analysis using remote sensing platform aimed for monitoring the environment. This approach utilizes satellite imagery data to identify vegetation health spatially at a specific time. Advances in remote sensing provide a framework for spatial and temporal analysis of vegetation indices, enabling the study of forest vegetation dynamics (Ramdani et al., 2024 ; Ruan et al., 2022 ). The Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) are two most commonly used VI methods in vegetation condition analysis. Previous research has analyzed the dynamics of mangrove forest coverage using the NDVI (Ruan et al., 2022 ) and EVI (Nepita-Villanueva et al., 2019 ) approach based on low resolution satellite data namely Moderate Resolution Imaging Spectroradiometer (MODIS). Where this formula can illustrate the dynamics of mangrove conditions globally, particularly in the Southeast Asia region. The latest study shows that mangrove vegetation cover in the Southeast Asia region, including Indonesia, has increased based on its vegetation index values (Ruan et al., 2022 ). Additionally, this study utilizes Landsat satellite data, which offers higher spatial resolution compared to MODIS, allowing for more detailed spatial variation. Therefore, this research is expected to provide a more detailed analysis of mangrove vegetation dynamics, particularly in National Park areas. We use the Enhanced Vegetation Index (EVI) is used as the vegetation index approach, offering greater sensitivity to vegetation density by incorporating the blue band along with the red and near-infrared bands (Huete et al., 2002 ; Ramdani et al., 2024 ). The findings of this study are expected to provide a more detailed overview of mangrove cover dynamics within National Parks areas, serving as valuable reference for future mangrove ecosystem management. 2. Material and Methods Study Area This study was carried out over the entire of Indonesia, with a particular emphasis on National Park regions that are connected to mangrove ecosystems. The boundary of the National Parks was obtained from the World Databased on Protected Areas (WDPA), which is managed by the United Nations Environment Programme World Conservation Monitoring Centre (UNEP-WCMC). The WDPA data can be accesses through the website https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA (UNEP and IUCN, 2005). During this time, we utilised the National Mangrove Map dataset that was developed by the Indonesian Ministry of Environment and Forestry (Ditjen PDASRH, 2021 ). This dataset was utilized to collect information regarding the spatial distribution of mangrove ecosystems. This data serves as the major reference map for the distribution of mangrove in Indonesia, which was previously analysed using data from multi-resolution remote sensing satellites. Upon completion of the preliminary identification procedure, it was discovered that twenty-three National Parks contain mangrove habitat and are located in coastal areas. Data This study utilizes satellite imagery from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager-Thermal Infrared Sensor (OLI-TIRS), all of which have a spatial resolution of 30 meters. The dataset spans the period from 1995 to 2024, with Landsat 5 covering 1995–1999, Landsat 7 (2000–2012), and Landsat 8 (2013–2024). Landsat is a medium-resolution satellite imagery with a pixel size of 30 meters and 16 days temporal resolution. The dataset used in this study encompasses the entire territory of Indonesia, in accordance with the distribution of National Parks. The data applied are Level 2A products, which have undergone geometric and radiometric corrections to provide surface reflectance values for improved analytical accuracy. Landsat is an optical satellite imagery system whose data quality is significantly influenced by cloud cover, particularly in tropical regions. To address this limitation, an annual median composite approach is applied to the dataset prior to vegetation index calculation, generating a single image per year with minimal cloud contamination. The images selected for compositing have cloud cover of less than 5%. One challenge during compositing is the sensor malfunction in Landsat 7 ETM + that caused the Scan Line Corrector (SLC) failure from 2004 to 2012. This issue is also addressed through the use of annual median compositing, which effectively represents the data for each year. The compositing process involves a maximum of approximately 22 raster images per year using the median statistic due to its robustness in representing the central tendency of time series data at each pixel (Diniz et al., 2019 ). All data processing was conducted using the Google Earth Engine (GEE) platform ( https://code.earthengine.google.com ), a cloud computing system with extensive server capacity for processing data in Earth Sciences. The GEE platform provides the capability to perform analyses on a large spatial scale using multiple satellite imagery data. To determine the extent of mangrove areas, we employed two primary data sources: the Global Mangrove Watch (GMW) and National Mangrove Maps (2021) from Indonesian Ministry of Environment and Forestry (KLHK). The National Mangrove Map is a unified dataset that represent the mangrove ecosystem cover across Indonesia and serves as a reference for mangrove cover analysis. This dataset was produced through satellite image analysis using both high and medium-resolution imagery, combined with various classification methods. However, the National Mangrove Maps does not cover all locations, as certain areas have undergone land cover change from mangrove to other land cover types, necessitating integration with additional datasets. Therefore, time series data is essential to capture these changes over time. By incorporating temporal analysis, the pattern of mangrove dynamics throughout the study period can be effectively observed. So, we utilize the overlaid GMW data from 1996 to 2020 combined with the National Mangrove map to produce a single dataset that serves as the Area of Interest (AOI) for mangrove areas. Subsequently, the unified mangrove dataset was clipped using the outer boundary polygon of the National Parks, which were previously obtained from the WDPA. We also use the Global Mangrove Watch (GMW) data to identify the changes of mangroves in each National Parks locations. This data generates from multi temporal Synthetic Aperture Radar (SAR) imagery from 1996–2020. All the dataset can be accessed freely from https://www.globalmangrovewatch.org/ and more discussion about this data can read in the (Bunting et al., 2022 ). Mangrove Enhanced Vegetation Index (EVI) Analysis To describe the dynamics of the mangrove ecosystem in the NP areas, we applied the statistical trend of Vegetation Index (VI). Where this index indicates the forest cover density based on the greenness values from satellite imagery data. The VI that is used for this research is the Enhanced Vegetation Index (EVI). This index was chosen due to its sensitivity for detecting the forest cover density more than another, as it incorporates the blue channel in its calculation process (Huete et al., 2002 ; Ramdani et al., 2024 ). The use of the blue band helps to reduce atmospheric interference, which is particularly significant for reflectance values in tropical regions such as Indonesia. The equation for calculating EVI is as follows (Huete et al., 2002 ): $$\:EVI=\:\frac{2.5\:(NIR-Red)}{NIR+6Red-7.5Blue+1}$$ Where NIR, Red, and Blue represent the reflectance values of the wavelength in Band 4, 3, and 1 for Landsat 5 and 7, as well as Band 5, 4, and 2 for Landsat 8. The EVI value ranges from − 1 to 1, where a higher value indicates denser vegetation cover. The annual EVI value is represented as a single spatial value, derived from the median composite over one year. To examine temporal variations in vegetation dynamics across each National Park, we applied a boxplot visualization technique. This method provides a statistical summary of annual EVI values in each National park with highlighting the median, spread, and presence of outliers within the dataset. Annual EVI values from 1996 to 2021 were aggregated for each National Park, allowing for a clear assessment of vegetation trends and fluctuations over time. Boxplots were used to compare variability among parks and to identify areas with consistent or changing vegetation conditions. Temporal trends in EVI were analyses by performing a linear regression at the pixel level, using year as the independent variable and EVI as the dependent variable. The significance of the slope value for each pixel is assessed using the p-value, with a maximum allowable threshold of 0.05 in this study. This means that only slope values with a p-value within the range of -0.05 < p-value < 0.05 are considered representative. Values outside this range are deemed statistically insignificant for depicting the dynamics of mangrove vegetation change. The results of the trend analysis were subsequently classified into two categories, namely Decrease and Increase, based on the slope values derived from the analysis. (Ramdani et al., 2024 ). Browning refers to areas with negative slope values, indicating a declining trend, whereas greening corresponds to positive slope values, reflecting an increasing trend. The proportion of mangrove area within the national park (NP) exhibiting either browning or greening trends was then calculated to determine the percentage of the total area represented by each category. 3. Result and Discussion 3.1. General Pattern of EVI in mangrove National Parks An overlay analysis of the distribution of mangrove ecosystems and conservation area maps indicates that around 23 National Parks in Indonesia encompass mangrove habitats, spanning an estimated 320,000 hectares (approximately 9.4% of the nation's total mangrove area) (Fig. 2 ). The majority of the National Park including extensive mangrove ecosystems is located in coastal areas adjacent to significant rivers. Two National Parks demonstrate substantial mangrove coverage: Lorentz National Park (LRZ) in Papua, with roughly 195,100.96 hectares (60.58% of its area), and Sembilang National Park (SBG) in Sumatra, with around 86,430.78 hectares of mangrove (26.83%). Conversely, the other 21 National Parks individually account for less than 3% of the overall mangrove coverage throughout all National Park regions. Extensive mangrove habitats are generally located in national parks along significant river estuaries, where the merging of freshwater and seawater, coupled with substantial sediment deposition, fosters optimal circumstances for mangrove growth. Nevertheless, mangrove ecosystems have also developed significantly in areas with relatively low sediment deposition, such as island regions. For instance, the Togean Islands (KTG) rank fourth in terms of mangrove ecosystem area within National Parks, covering approximately 6,315.18 hectares. Although this represents a relatively small percentage, it is noteworthy when compared to mangrove ecosystems located in riverine or estuarine regions, which typically exhibit higher levels of sediment accumulation. Next, several National Parks support relatively small mangrove areas, including Berbak (BRB) with 64.96 hectares, Siberut (SBR) with 132.67 hectares, and Manusela (MNS) with 288.31 hectares. In general, mangrove areas with limited spatial extent are typically found in coastal zones characterized by sandy or rocky substrates with minimal mud deposition. Other parks also exhibit limited mangrove coverage, highlighting variability in the spatial distribution of mangrove ecosystems across the National Park network. For the complete data of the mangrove area coverages in each National Park can be seen in the Supplementary Material 1. The analysis of Enhanced Vegetation Index (EVI) values over a 30-year period (1995–2024) across 23 national parks in Indonesia reveals substantial spatial variability in vegetation coverages and condition. Across all National Park locations, the average EVI value specifically within mangrove areas was found to be 0.44 ± 0.08 based on 30 years of temporal data. Figure 3 shows the boxplot illustrating the temporal variation of EVI throughout the entire study period in each National Park. This value indicates that mangrove ecosystem coverage in National Park areas falls within the moderate category. Among all sites, Berbak (BRB) and Sembilang (SBG) exhibited the highest mean EVI values, at 0.60 and 0.54, respectively. These were closely followed by Gunung Palung National Park (GNP), with a mean EVI of 0.52. These three areas also exhibited relatively high upper quartile (Q3) values, suggesting that much of the EVI data consistently exceeded 0.5, reflecting dense and healthy vegetation cover throughout the study period. Conversely, Kepulauan Seribu (KPS), a small island ecosystem located in Jakarta Bay, recorded the lowest mean EVI (0.2145), with observed values ranging between 0.1398 and 0.3219. Similarly, Komodo (KMD) (0.2934) and Bali Barat (0.3548) also exhibited low average EVI values. These results are indicative of their naturally arid environmental conditions and more open vegetation structures, typical of the drier eastern Indonesian archipelago. National parks located in Sulawesi and Papua, such as Wakatobi (WKT), Rawa Aopa Watumohai (RAW), and Wasur (WSR), displayed moderate mean EVI values, ranging from 0.4296 to 0.4504. These areas also exhibited notable variability in vegetation conditions, as indicated by the wider interquartile ranges (IQR) between the 25th (Q1) and 75th (Q3) percentiles. In particular, parks such as Manusela (MNS) and Kutai (KTI) showed considerable EVI spread, suggesting the presence of temporal or spatial heterogeneity likely driven by seasonal changes, mixed vegetation types, and potential anthropogenic disturbances. 3.2. Trend in EVI Mangroves of National Park The EVI trend analysis was conducted both spatially and by averaging all pixel values within each National Park. The average trend of EVI changes for each National Park is presented in Table 1 , showing the percentage of areas experiencing an increasing or decreasing trend. Figure 4 displays the spatial trend analysis results for selected National Parks with the largest mangrove areas, namely Lorentz (LRZ), Sembilang (SBG), Wasur (WSR), and Kepulauan Togean (KTG). The spatial presentation of the analysis results has undergone a masking process in which only pixel trend values with a p-value less than 0.05 (95% confidence level) are shown. Among the four National Parks with the largest mangrove extent, the distribution of EVI trend values is quite varied. In the LRZ area, most pixels exhibit relatively small trend values, ranging between 0.00 and 0.005. This range indicates that changes in mangrove cover are relatively minor. Some specific points, especially in coastal areas, appear greener, indicating trend values greater than 0.01, which means more significant mangrove growth. However, in several locations—both along the coastline and in inner river estuaries—red and orange colors appear, indicating negative trend values (< 0.00). The analysis results show that 92.918% of the mangrove area in LRZ experienced an increase in EVI, while the remaining 7.082% experienced a decrease (Table 1 ). The next National Park with the largest mangrove area is Sembilang (SBG). The EVI trend analysis in this region reveals that several inland mangrove areas, located relatively far from the coastline, tend to exhibit an increasing vegetation index, with trend values ranging from 0.005 to 0.01 (Fig. 4 b). In contrast, areas showing a decreasing EVI trend are predominantly located along the coastal fringe. The proportion of mangrove areas in SBG experiencing an increasing EVI trend is 95.090%, while 4.910% of the area shows a decreasing trend. A similar pattern is observed in Wasur National Park (WSR), where the majority of areas with decreasing EVI trends are concentrated along the coastal mangrove zones, accounting for 9.651% of the total mangrove coverage (Fig. 4 c). The remaining 90.349% of mangrove areas, predominantly located further inland, exhibit increasing EVI trends. The fourth National Park with extensive mangrove cover is the Togean Islands (KTG). In this region, the increase in EVI is substantial and widespread, with 94.231% of the area showing positive trends, while only 5.769% exhibits a decline in EVI values. It is important to note that the KTG region has distinct characteristics, being a karst archipelago with minimal riverine input. Table 1 presents the results of the trend analysis of the annual mean values of the Enhanced Vegetation Index (EVI), along with the R 2 (R-Squared) values for each National Park. The table also includes the percentage of mangrove areas that experienced either an increase or a decrease in EVI over the study period. Overall, the avegare EVI values in mangrove areas show an increasing trend across all National Parks. The mean EVI trend for mangrove regions throughout the National Park network is 0.0025 ± 0.001 per year. The analysis reveals that 90.48% of mangrove areas within National Parks experienced an increase in EVI, indicating overall improvements in vegetation condition, while the remaining 9.52% of the area experienced a decrease. The highest average increase in EVI is observed in Kepulauan Seribu (KPS), with a rate of 0.0061 per year. This is followed by Manusela (MNS), Wakatobi (WKT), Alas Purwo (APW), and Kepulauan Karimunjawa (KRJ), each exhibiting an average annual increase of 0.0039. This parks are indicative of strong positive trends in mangrove vegetation greeneess and density. In contrast, the smallest increses in EVI were recorded in Way Kambas (WKB) and Ujung Kulon (UJK) with trend values of only 0.001 per year, suggesting relatively stagnant or slower vegetation recovery in these areas. The R 2 values in this study show different levels of confidence in the linear trend models, ranging from 0.206 (UJK) to 0.88 (MNS). While lower R 2 suggest more variability or noise in the data overtime and higher R 2 show stronger temporal consistency in EVI change. To better illustrate the trend patterns for each National Park, individual EVI trend graphs are provided in Supplementary Material 2. Table 1 Trend of EVI in Mangroves Area, Percentages area of increase (decrease) with significant trend over the period of 1995–2024. No National Park Slope of EVI Annual Mean Mangrove Area Percentages (%) Area of Increase EVI Percentages (%) Area of Decrease EVI R 2 1 Lorentz (LRZ) 0.0019 92.918% 7.082% 0.6485 2 Sembilang (SBG) 0.0024 95.090% 4.910% 0.4576 3 Wasur (WSR) 0.0026 90.349% 9.651% 0.4184 4 Kepulauan Togean (KTG) 0.0026 94.231% 5.769% 0.7908 5 Rawa Aopa Watumohai (RAW) 0.0022 88.572% 11.428% 0.6592 6 Kutai (KTI) 0.0017 81.874% 18.126% 0.3326 7 Gunung Maras (GMS) 0.0016 90.156% 9.844% 0.4791 8 Tanjung Puting (TJP) 0.0014 83.587% 16.413% 0.3896 9 Teluk Cenderawasih (TLC) 0.0021 88.967% 11.033% 0.8136 10 Ujung Kulon (UJK) 0.0010 80.676% 19.324% 0.2061 11 Bunaken (BNK) 0.0035 94.193% 5.807% 0.6393 12 Komodo (KMD) 0.0016 80.767% 19.233% 0.4541 13 Way Kambas (WKB) 0.0011 81.073% 18.927% 0.2661 14 Alas Purwo (APW) 0.0039 98.429% 1.571% 0.6736 15 Wakatobi (WKT) 0.0039 96.496% 3.504% 0.7753 16 Bali Barat (BLB) 0.0038 97.441% 2.559% 0.8044 17 Kepulauan Karimunjawa (KRJ) 0.0039 96.038% 3.962% 0.7459 18 Gunung Palung (GNP) 0.0021 97.021% 2.979% 0.23 19 Baluran (BLR) 0.0025 90.694% 9.306% 0.6338 20 Manusela (MNS) 0.0039 89.057% 10.943% 0.8815 21 Siberut (SBR) 0.0021 84.010% 15.990% 0.5355 22 Berbak (BRB) 0.0031 93.634% 6.366% 0.2416 23 Kepulauan Seribu (KPS) 0.0061 95.667% 4.333% 0.8475 The ranking of NP is based on the extent of mangrove area. All trend values have a p-value of less than 0.05. A negative (-) or positive (+) value indicates a decrease or increase in mangrove area from 1996 to 2020, respectively. The notation "NaN" means that mangroves in the area were not detected based on GMW data. Nearly all National Parks have a largely positive EVI trend across all sites, with variations in the amount of changes and the extent of area impacted. Although most mangrove regions show a rise in EVI, few parks display significant sections experiencing reduction. For example, while KTS and KMD exhibited favorable EVI trends, they also reveal significant proportions of mangrove area with declining EVI (18.13% and 19.29%, respectively) (Fig. 5 ). Likewise, WKB and UJK observed reductions of 18.93% and 19.32% in their mangrove areas, which similarly exhibited declining EVI. These data may suggest localized disturbances or degrading pressures impacting segments of the ecosystem, notwithstanding the overall favorable trend. Conversely, some National Parks, including APW, BLB, and KRJ, have over 95% of their mangrove areas undergoing rises in EVI, underscoring regions of significant vegetation enhancement. This variety highlights the necessity for tailored monitoring and management techniques to address regions experiencing a decline in vegetation health. Figure 5 illustrates the regional distribution of various National Parks that demonstrate the most significant reductions in EVI. Figure 4 a illustrates UJK, situated in the western region of Java Island. The mangrove ecosystems in UJK comprise two primary regions: Java Island and Panaitan Island. The majority of the declining EVI trends (red regions) are focused on the eastern side of Panaitan Island, with smaller patches noted at the tip of Java Island. Conversely, the mangroves on the Java Island side, forming the core zone of UJK, exhibit relative stability, with certain regions displaying a favourable vegetation index trend (green patches). The following area is KMD (Fig. 5 b), an archipelagic zone distinguished by high tourism activity and extensive development. A significant part of mangrove habitats exhibiting decreasing EVI trends is found on the western side of Komodo Island, with lesser impacted regions on Padar and Rinca Islands. Numerous smaller islands within KMD also demonstrate declining EVI patterns. KTI (Fig. 5 c) is situated in the eastern region of Kalimantan, specifically inside East Kalimantan Province. In this region, the majority of mangrove areas exhibiting a drop in EVI are located in the northern part of KTI, frequently near settlements. The red areas in KTI are primarily located inland inside the mangrove ecosystem, distant from the coastline, suggesting that deterioration is mainly influenced by terrestrial factors. In contrast, mangroves situated along the coastline exhibit a rising EVI trend. WKB is situated in Lampung Province on Sumatra Island. The mangrove ecosystems in this area are very limited, primarily situated along the eastern coastline. Certain regions demonstrating a drop in EVI are located in the southern section of WKB, specifically inside mangrove zones that extend into the sea (headland areas). Concurrently, other mangrove regions within WKB often exhibit an ascending EVI trend. The Global Mangrove Watch (GMW) data was utilized to analyze the alterations in mangrove cover area from 1996 to 2020 (Fig. 6 ). This enabled us to assess both the increase and decrease in EVI values. The study's findings indicate that, of the twenty-three National Park sites, over half experienced a decline in mangrove coverage, and the other sites demonstrated an increase. During this period, there was a total reduction of 839.41 hectares in mangrove cover across the National Parks. KMD (-11.79%), KTI (-6.55%), and MNS (-7.75%) incurred the most losses. The statistics given herein corroborate previous findings, which identified KMD and KTI as the national parks that had the most substantial declines in EVI values. In contrast, APW and BLB recorded the most significant increases in mangrove cover, with increased cover of 17.15 and 10.09 percent, respectively. It was determined that both locations are situated along the same coastal region adjacent to the Bali Strait. Moreover, it was shown that 97% of their entire area is demonstrating rising EVI trends, representing the highest proportion among all national parks. Analysis of the region revealed that SBG experienced the most substantial decline in mangrove cover throughout the evaluation period, totaling − 989.75 ha, followed by KTI at -260.04 ha, and RAW at -150.621 ha. Nevertheless, the proportion of mangrove acreage in these locations may be rather insignificant compared to the total mangrove area within the National Park. Concurrently, LRZ experienced the most substantial increase in mangrove area, amounting to 569.70 hectares, followed by APW at 144.14 hectares. For further details concerning the area alterations, please refer to Supplementary Material 3. Discussion The spatial distribution of mangrove habitats within Indonesia's National Parks covers nearly all regions, whether they are large or small islands. Mangroves, which flourish in tropical climates, are primarily located in national parks that feature vast swamps and significant river estuaries. This pattern is illustrated by three National Parks: LRZ, SBG, and WSR, which host the most extensive mangrove ecosystems in Indonesia, all located within vast estuarine and swamp-dominated lowland areas. It is important to highlight that LRZ and WSR are located in the southern lowland swamps of Papua Island, an area recognised as one of the largest wetland regions in Indonesia (Jia et al., 2023 ). These areas serve as major sediment deposition zones that have accumulated over long geological periods, supporting the development of some of the most expansive mangrove ecosystems in Indonesia (Brunskill et al., 2004 ; Setyadi et al., 2021 ). In contrast, SBG is located within the estuarine systems of the Musi and Batanghari rivers along the eastern coast of Sumatra (Purwanto et al., 2022 ). Mangroves necessitate particular environmental conditions, including suitable substrate types like mud or sand, along with exposure to seawater salinity to facilitate their growth and reproduction (Boubakary et al., 2024 ; R. S. Costa et al., 2016 ). Estuarine environments are particularly suitable for the development of mangroves because of their favourable substrates and steady supply of organic matter, which is essential for the physiological processes that support mangrove metabolism (Li et al., 2025 ; Sarker et al., 2021 ). In addition to the extensive mangrove ecosystems found in estuarine lowlands. Several National Parks in Indonesia also host mangrove communities within island and karstic environment, which smaller in spatial extent and ecologically significant and represent a distinct mangrove biogeographic setting. A notable example is KTG in Central Sulawesi, where mangroves cover approximately 6,315.28 ha, thriving on carbonates substrates and limestone dominated coastal zones (Rositasari et al., 2023 ; Sendjaja et al., 2020 ). Similarly with KMD in East Nusa Tenggara and MNS on Seram Island (Maluku Province) also feature island based mangrove situated in volcanic or karst landscapes with limited freshwater inflow and minimal sediment depositions (Cortés-Esquivel et al., 2023 ; Henri et al., 2024 ; Terzić et al., 2021 ). These area are generally characterized by higher salinity levels, shallow soils, and steeper coastal gradients that all of which impose ecological constraints that select for salt tolerant and stress adapted mangrove species (Godoy et al., 2018 ; Medina-Calderón et al., 2021 ). The mangrove species that dominate such as environments often include Sonneratia sp , Avicennia sp , and other taxa with specialized adaptations such as pneumatophores, salt excreting leaves, and robust root systems capable of anchoring in rocky or compacted substrates (Feng et al., 2020 ; Hao et al., 2021 ). Although species richness in these island system may be lower compared to those in nutrient rich estuarine, they perform critical ecological functions, including shoreline stabilization, sediment trapping, and serving as nurseries for another biotas around mangrove ecosystems (Basyuni et al., 2021 ; Manna et al., 2010 ). Based on satellite-derived assessments, the averages of EVI values of Mangrove Ecosystems across Indonesia’s National Parks indicate a moderate level of vegetation density and greenness (Nepita-Villanueva et al., 2019 ; Ramdani et al., 2024 ). This suggest that many mangrove stands exhibit relatively healthy canopy conditions, although spatial variability in vegetation quality remains evident across sites. Notably, The BRB, SBG, and GNP National parks recorded the highest EVI values, reflecting dense canopy structure and generally healthy vegetation status (Ishtiaque et al., 2016 ; Samanta et al., 2021 ). These favorable conditions are likely influenced by supportive environmental factors such as sustained freshwater input, nutrient enriched estuarine systems, and minimal anthropogenic disturbance. For example, BRB and SBG are located at the mouth of major rivers on the eastern coast of Sumatra (Abdillah et al., 2024 ), which enhances nutrient availability and hydrological stability (Forouzannia & Chamani, 2022 ; Phong & Luom, 2021 ). Furthermore, their relative remoteness from human settlements may contribute to reduced anthropogenic pressure, thereby supporting more stable and healthy mangrove ecosystems. Moreover, the KPS, KMD, and BLB National Parks recorded as the lowest mean EVI values, ranging between 0.21 to 0.35. These relatively low values are indicative to naturally arid climate conditions, rocky or sandy substrates, and limited freshwater or sediment inflow (M. Adame et al., 2021 ; M. T. Costa et al., 2019 ). These environmental constraints are particularly common in the eastern Indonesian regions and small islands conditions, where mangrove growth tends to be patchy and sensitive to water availability and salinity stress. While the mangrove cover in mainland regions generally exhibits higher density due to favorable growth conditions (Cahyo et al., 2024 ; Najamuddin et al., 2024 ). The examination of EVI trends spanning three decades indicates a promising pattern throughout Indonesia’s National Parks, with around 90.48% of the total mangrove areas showing a positive trend in vegetation greenness. This notable rise indicates that mangrove ecosystems in these protected regions are generally undergoing enhancements in canopy cover and overall vegetation health, a favourable trend driven by a variety of ecological and human-induced factors. The continuous conservation initiatives, especially within effectively managed national parks, could have promoted the natural regeneration and restoration of degraded mangrove areas. Furthermore, the extensive mangrove areas, including LRZ, SBG, and WSR, are situated in remote or sparsely populated regions, resulting in minimal human-induced disturbances. This condition facilitates the functioning of ecological processes with minimal external influences. The enduring legal protection status of these regions likely contributed significantly to the reduction of deforestation, illegal logging, and land conversion, thus fostering stable or rising vegetation trends (Bonannella et al., 2024 ). In various inland and estuarine mangrove areas, observed positive trends could indicate sediment accretion, hydrological stability, and suitable salinity conditions, which are essential environmental factors for promoting mangrove growth. In Sembilang National Park (SBG), the expansion of mangroves along the Musi River estuary is facilitated by ongoing sediment deposition and stable hydrodynamic conditions, which create an ideal substrate for regeneration. In Berbak National Park (BRB), the mangrove areas adjacent to the Batanghari River delta show encouraging trends. The combination of sediment input from upstream and estuarine nutrient dynamics has played a significant role in supporting and promoting mangrove growth in these regions (Prasad & Ramanathan, 2008 ; Wösten et al., 2003 ). Notwithstanding the generally favourable trend of mangrove greenness in Indonesia’s National Parks, specific localised regions continue to demonstrate decreasing EVI trends, especially along coastal margins and inside small island ecosystems. The KMD National Park has exhibited significant negative EVI trends in particular areas. This is linked to its designation as one of Indonesia’s Super Priority Tourism Destinations, attracting substantial annual visitor numbers (Hidyarko et al., 2021 ; Mahmudin et al., 2024 ; Miftah Wirakusuma et al., 2024 ). Mangrove ecosystems in KMD are frequently deprioritized in conservation efforts relative to tourism development, leading to increased ecological stresses and insufficient conservation focus (Macamo et al., 2024 ). A similar situation can be seen in KPS National Park, which is located in the urban region of Jakarta. There are considerable portions that have a falling EVI. As the marine and coastal protection area that is geographically closest to the capital, the KPS is subject to a high amount of anthropogenic stressors (Setiawati et al., 2023 ; Yonvitner et al., 2022). These stressors include urban expansion, industrial discharges, coastal reclamation, and extensive fishing operations. All of these stressors place a tremendous amount of strain on the mangrove ecosystems that are located inside the KPS. Simultaneously, in UJK National Park, the emergence of localised adverse trends underscores the susceptibility of mangroves located near human activities, shipping lanes, and coastal communities. The findings indicate that although national conservation efforts have successfully preserved favourable mangrove conditions, localised management challenges persist in areas where tourism development, urbanisation, and direct human pressures intersect with mangrove ecosystems. The investigation indicated that KMD, KTI, UJK, and WKB are the regions exhibiting the most pronounced decrease in EVI. This trend aligns with the Global Mangrove Watch Data indicating a reduction in mangrove area from 1996 to 2020, signifying both functional and physiological deterioration of mangrove ecosystems. The decline of mangrove in KMD is mostly attributable to proliferation of tourist, coastal development project, and unsustainable activities. The distinctive geographical features of these islands, including particular geomorphological development and restricted freshwater runoff, can affect the natural regeneration and dynamics of mangrove ecosystems (Debrot et al., 2019 ; Fujimoto, 1997 ), whereas the increasing demands for tourism infrastructure have markedly expedited habitat conversion (Duvat et al., 2019 ; Majiid et al., 2023 ; Rimba et al., 2020 ; Waleed et al., 2025 ; Yusuf et al., 2017 ). The mangroves in KTI are detached from terrestrial ecosystems, rendering them more susceptible to the encroachment of human developments and direct anthropogenic influences (Blanco-Libreros & Ramírez-Ruiz, 2021 ; Panggabean et al., 2023 ). The observed decline in EVI in this region primarily occurred around coastal communities, signifying significant anthropogenic impacts (Gonçalves et al., 2019 ). This conversion of mangrove ecosystems into agricultural or aquaculture land is prevalent in Indonesia, particularly in Kalimantan Island, which is significantly affected, along with the KTI region (Ilman et al., 2016 ). Conversely, Ujung Kulon (UJK) and Way Kambas (WKB) demonstrated a reduction in EVI mostly in coastal regions distant from direct human settlement, indicating that climate change and coastal erosion are the principal contributing variables. The southern waters of Java are notably susceptible to severe wave activity, particularly in the Sunda Strait region. This region is subject to significant risk of coastal erosion along its coastlines. Elevated sea levels, coastal erosion, and enhanced hydrodynamic activities are probabl1e factors in the noted deterioration. This pattern demonstrates a dual dynamic: manmade drivers, such as land conversion and tourism, are more significant in KMD and KTI, whereas natural drivers associated with climate variability and coastal processes exert a higher influence in UJK and WKB. These findings underscore the necessity for conservation strategies customized to local circumstances, balancing the regulation of human activities with the implementation of adaptive measures to alleviate the effects of climate change. Moreover, the susceptibility of these coastal ecosystems is exacerbated by their restricted natural recuperative ability, especially in regions experiencing substantial human impacts or pronounced geomorphological alterations. The management of national parks in Indonesia is now conducted directly by the Ministry of Forestry. The overlay study indicates that 23 national parks contain mangrove forests; nevertheless, only three of these parks (LRZ, SBG, and GMS) had mangrove regions exceeding 10% of their overall area. Most other parks have a mangrove fraction below 5%, often including lowland forest regions with coasts that include mangrove habitats. As of yet, no National Park in Indonesia has been established as a management area exclusively for mangrove ecosystems. The majority of mangrove forests in Indonesia are classified as Protected Forest and Production Forest (Ilman et al., 2016 ). The distribution of mangroves inside National Parks may be classified into two primary categories: those situated in vast river estuaries and those found on tiny islands. In mangrove regions located in extensive river estuaries, substantial large-scale alterations in mangrove coverage inside National Parks have often not transpired. Only a limited number of parks, including SBG and KTI, have seen significant alterations due to land use conversion and forest fires (Dwiyahreni et al., 2021 ; Guild et al., 2022 ). Three decades of EVI trends have given us some information, but we still need to perform more research to fully understand how mangroves work in Indonesia's Conservation Management Areas. To better understand the connection between canopy greenness and ecosystem performance, future research should include both satellite-derived vegetation indices and field estimates of biomass, carbon storage, and species composition. To make conservation efforts fairer, we need to look at social and biological factors, such as how much the local community depends on mangroves and how tourism and aquaculture affect them. Predictive modelling can look at how mangrove ecosystems are affected by climate change, hydrodynamic processes, and changes in land use to see how vulnerable and strong they are. In the future, researchers may use remote sensing, ecological monitoring, and socio-economic frameworks to help set conservation goals and adapt management in Indonesia's National Parks, which are known as important Management Areas of Conservation. Conclusion This research was finding the spatio-temporal dynamics of mangrove vegetation health in Indonesia’s National Parks through the use of the Enhanced Vegetation Index (EVI) for the years 1995–2024. The mean EVI for all National Parks was 0.44, signifying that mangroves are typically in moderate condition. Over 80% of mangrove regions exhibited a rising EVI trend, indicating a general increase in ecosystem vitality. The APW, BLB, and GNP regions demonstrated the largest percentage of rising EVI values, with APW further noting the most significant increase in mangrove areas and the highest average EVI. KMD and KTI had the highest percentage of diminishing EVI trends, which aligned with decreases in mangrove coverage. The decreases were mostly linked to human-induced disruptions, including land-use changes, forest fire and unsustainable tourism practices. The overall trends demonstrate that National Parks significantly contribute to the preservation of mangrove ecosystems, as evidenced by the comparatively fewer negative trajectories relative to favourable ones. This finding indicates that the protected area concept has yielded quantifiable conservation advantages for mangroves, although localised pressures still pose a substantial issue. Considering that Indonesia currently lacks a national park specifically focused on mangrove protection, the creation of such protected areas could enhance national conservation initiatives. A future study must include evaluations of ecosystem services, such as carbon sequestration, and utilise dynamic system modelling to facilitate adaptive management and ensure the long-term viability of mangrove ecosystems in national parks. Declarations Funding Declaration We would like to thank the Ministry of Higher Education, Research, and Technology of the Republic of Indonesia for providing funding for this research and publication through the Fundamental Research Regular scheme, fiscal year 2025, under Contract Number B/041/UN46.1/PT.01.03/BIMA/PL/2025. Ethics and Consent to Participate declarations: not applicable Author Contribution H.A.R : Conceptualization, Writing-Original Draft, Investigation, Formal Analysis, Methodology, Visualization; Z.H: Investigation, Review and Editing; A.R.A : Conceptualization, Investigation, Formal Analysis, Review and Editing; M.K.W : Review and Editing, Funding Acquisition; All author reviewed the manuscript References Abdillah, M., Utomo, B., & Sulistioadi, J. (2024). Distribution of density and zoning patterns of mangrove forest in eastern coastal Sumatera, Indonesia . 1352 (1), 012045. Adame, M. F., Connolly, R. M., Turschwell, M. P., Lovelock, C. 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maps)\u003c/p\u003e","description":"","filename":"Figure1DistributionofNationalParkwithMangroveEcosystem.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7554686/v1/80af48b5118e60cd11ab83bd.jpeg"},{"id":93704966,"identity":"bf4e9e3c-3e67-40aa-9465-6efb0bc605d4","added_by":"auto","created_at":"2025-10-16 16:14:01","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":406135,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Mangrove Ecosystem in all National Parks\u003c/p\u003e","description":"","filename":"Figure2DistributionofMangrovEcosysteminEachNationalPark.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7554686/v1/eec5013f2ea067d8e0258550.jpeg"},{"id":93704969,"identity":"3aee9156-8105-4289-981f-31cf0d18f262","added_by":"auto","created_at":"2025-10-16 16:14:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":459122,"visible":true,"origin":"","legend":"\u003cp\u003eBox plot of EVI value in all National Park\u003c/p\u003e","description":"","filename":"Figure3EVIBoxplot.png","url":"https://assets-eu.researchsquare.com/files/rs-7554686/v1/5e59c4da79828aa9480a94b3.png"},{"id":93705306,"identity":"467db699-8528-4f98-b4cc-e0cf565f7a27","added_by":"auto","created_at":"2025-10-16 16:22:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":911499,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial Trend EVI in the Largest Mangrove Area of National Parks a. Lorentz (LRZ); b. Sembilang (SBG); c. Wasur (WSR); and d. Kepulauan Togean (KTG)). The trend values shown have a p-value of 0.05 (95% confidence level).\u003c/p\u003e","description":"","filename":"Figure4TrendLargestTNthatHavemangrove.png","url":"https://assets-eu.researchsquare.com/files/rs-7554686/v1/76f7e026bdd8f263bf0ad844.png"},{"id":93705310,"identity":"a06c2832-ce91-4e65-b5c1-7008df5583b0","added_by":"auto","created_at":"2025-10-16 16:22:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":665136,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of the largest Percentages of Decreases areas based on Table 1 (A) Ujung Kulon (UJK); (B) Komodo (KMD); (C) Kutai (KTI); (D) Way Kambas (WKB).\u003c/p\u003e","description":"","filename":"Figure5LargetsChanges.png","url":"https://assets-eu.researchsquare.com/files/rs-7554686/v1/11ce5de2df3d3106652b2e63.png"},{"id":93705307,"identity":"4465a9db-164d-4517-a555-8c4d64dbbba8","added_by":"auto","created_at":"2025-10-16 16:22:01","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":886959,"visible":true,"origin":"","legend":"\u003cp\u003eChanges of mangrove area coverages in each National Parks based on Global Mangrove Watch data (Bunting et al., 2022).\u003c/p\u003e","description":"","filename":"Figure6AreaChanges.png","url":"https://assets-eu.researchsquare.com/files/rs-7554686/v1/e56ea716d6c8b1771aea7deb.png"},{"id":93958995,"identity":"64f630c9-49b5-4f11-9332-248e7173c654","added_by":"auto","created_at":"2025-10-20 16:48:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4661952,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7554686/v1/2206f060-9805-422a-be4a-4f155a4ffffd.pdf"},{"id":93704972,"identity":"01cd845a-5a31-430a-9d44-2cfbc65d01b9","added_by":"auto","created_at":"2025-10-16 16:14:01","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2116098,"visible":true,"origin":"","legend":"","description":"","filename":"SuplementaryFilesSubmit.docx","url":"https://assets-eu.researchsquare.com/files/rs-7554686/v1/254e3c018bebca3e86cb096e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Long-Term Dynamics of Mangrove Vegetation Coverages in Indonesia’s National Parks Derived from Remote Sensing Data","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMangroves areas is category of coastal vegetation that play crucial role on climate change mitigation, disaster resilience, and preserving coastal biodiversity (Hamza et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Macintosh et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Segaran et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Mangrove ecosystems hold significant ecological and economic importance in coastal areas. They serve crucial roles as habitats for a wide variety of marine organisms, such as fish and crustaceans, providing essential nourishment and protection from the conditions of the open ocean (Carrasquilla-Henao et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nozarpour et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As an estuarine ecosystem, mangroves serve as the terminal point for terrestrial runoff and riverine discharge, resulting in the accumulation of both organic and inorganic materials transported from the land (Jennerjahn \u0026amp; Ittekkot, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). This leads to exceptionally high productivity within the mangrove ecosystem, enabling it to supply food for a wide variety of organisms that inhabit it (Nagelkerken et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In the context of climate change, mangrove ecosystems function as highly effective carbon sinks, exhibiting superior carbon storage and sequestration capacity compared to other tropical forest types (M. F. Adame et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Mangroves also have function in protecting coastal areas from various climate change-related threats, such as sea level rise, strong winds, and storms (Van Hespen et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). From an economic perspective, mangrove ecosystems support the livelihoods of coastal communities by providing resources such as harvestable marine species, timber, and opportunities for ecotourism (Blanton et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAs a tropical region, Indonesia provides a suitable habitat for mangroves, particularly in areas with large river estuaries (Bhowmik et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Moschetto et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). According to the National Mangrove Map from 2021 (Ditjen PDASRH, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Indonesia boast a total mangrove forest area of 3.4\u0026nbsp;million hectares. The distribution of mangrove ecosystem spans from the western to eastern region including the eastern coast of Sumatera, Kalimantan, the Northen Coast of Java, Sulawesi, and the western coast of Papua. These areas are primarily located in estuarine environments formed by major river systems in Indonesia. Indonesia\u0026rsquo;s mangrove ecosystems are distributed across various biogeographic regions and ecological settings. On the larger islands characterized predominantly as lowland mangrove areas are typically concentrated in expansive river estuaries, where fine sediment accumulation supports the development of dense and extensive mangrove stands (Cinco-Castro et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Walsh \u0026amp; Nittrouer, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). These systems are particularly prevalent in western Indonesia, including Sumatra, Java, and Kalimantan (Kusmana, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Rahman et al., 2024). Nevertheless, substantial lowland mangrove areas are also present along the southern coastal fringes of Papua (Setyadi et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In contrast, mangrove assemblages in smaller islands and archipelagos often establish on karstic substrates and are directly exposed to open ocean conditions. These settings are generally associated with lower precipitation regimes, often classified under semi-arid climatic zones (M. Adame et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Thivakaran et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Such ecological differences lead to notable variation in mangrove structure, density, and species composition when compared to the more expansive lowland or estuarine mangrove systems (Dunham, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Torres et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMangrove ecosystems in coastal regions face significant pressure from environmental factors and human activities (Gitau et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Maina et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These vital ecosystems face threats from human activities like deforestation, pollution, and land reclamation for farming and urban growth (Bhowmik et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Moschetto et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The expansion of aquaculture, particularly shrimp farming, has resulted in a significant loss of mangrove cover (Akber et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Herbeck et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). More than 62% of mangrove loss caused by human activities has occurred over the past two decades (Goldberg et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition, other drivers of mangrove ecosystem degradation have been exacerbated by climate change, including sea level rise, extreme weather events, and increasing of the global sea surface temperature (Alongi, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Friess et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lovelock et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ward et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These phenomena pose significant threats to the health and distribution of mangrove ecosystems (Chung et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ward et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The degradation of mangrove forest not only harms biodiversity but also impacts communities relying on mangroves for fish, timber, Preserving and coastal protection (Asari et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Cahyaningsih et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Carugati et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Mangroves is crucial for ecological balance, marine life support, and climate change mitigation (Kumari \u0026amp; Rathore, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Conservation strategies must address both human and natural threats to sustain these vital coastal areas (Arifanti et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hidayah et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe presence of mangrove within conservation zones represents an effort to protect this ecosystems from threats and degradation. The rationale behind safeguarding these estuarine habitats within conservation zones stems from their immense ecological value and the critical ecosystem services they provide. Mangrove are renowned for their role as nurseries for a diverse array of marine life, serving as natural barriers against coastal erosion and storm surges, and acting as significant carbon sinks that mitigate climate changes impact (Arifanti et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Murdiyarso et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Uddin et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Within Indonesia\u0026rsquo;s comprehensive network of protected areas, National Parks play a particularly crucial role in mangrove conservation. Based on mangrove coverages in Indonesia from National Mangrove Maps, over than 22.2% has been designated for conservation efforts, including national parks (Ditjen PDASRH, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, few National Parks explicitly designate mangrove ecosystems as their primary management focus. Only Sembilang and Lorentz National Parks have established mangroves as the central objective of their conservation and management efforts (Claudino-Sales, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Silvius et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). While other National Park mostly coverages by Low-Highland Forest that some of these have the coastal areas and have mangrove ecosystem.\u003c/p\u003e\u003cp\u003eMost other National Parks in Indonesia are predominantly terrestrial forest ecosystems, some of which include coastal zones with mangrove areas. As a form of conservation area scheme, National Parks play a critical role in protecting mangrove cover. Ideally, these park could serve as role models for mangrove conservation efforts whether managed by governmental institutions or through collaborative engagement with local communities. However, there remains a paucity of comprehensive research specifically addressing the dynamics of mangrove cover across all National Parks in Indonesia. The major challenges lies in the extensive distribution of mangrove areas, with some located in remote and inaccessible regions. Given the spatial extent and dispersal of mangrove ecosystems within National Parks, remote sensing approaches supported by cloud computing platform offer a promising solution for monitoring and analysis. Existing remote sensing based studies have typically focus on individual Parks, such as Sembilang (Purwanto et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Alas Purwo, Lorentz (Setyadi et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and Kutai (Budiarsa \u0026amp; Rizal, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Najamuddin et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), rather than providing a holistic national scale assessment. This study will undertake a comprehensive examination of the dynamics of mangrove vegetation changes throughout all National Park regions in Indonesia using remote sensing data.\u003c/p\u003e\u003cp\u003eThe assessment of forest condition status can be carried out by analyzing Vegetation Index (VI) values based on Remote Sensing data, which provide insight of health, density, and overall state of forest ecosystems (Akbar et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Arshad et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chellamani et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Hidayah et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Vidhya et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This method involves observing the Earth Observation Analysis using remote sensing platform aimed for monitoring the environment. This approach utilizes satellite imagery data to identify vegetation health spatially at a specific time. Advances in remote sensing provide a framework for spatial and temporal analysis of vegetation indices, enabling the study of forest vegetation dynamics (Ramdani et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ruan et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) are two most commonly used VI methods in vegetation condition analysis. Previous research has analyzed the dynamics of mangrove forest coverage using the NDVI (Ruan et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and EVI (Nepita-Villanueva et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) approach based on low resolution satellite data namely Moderate Resolution Imaging Spectroradiometer (MODIS). Where this formula can illustrate the dynamics of mangrove conditions globally, particularly in the Southeast Asia region. The latest study shows that mangrove vegetation cover in the Southeast Asia region, including Indonesia, has increased based on its vegetation index values (Ruan et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, this study utilizes Landsat satellite data, which offers higher spatial resolution compared to MODIS, allowing for more detailed spatial variation. Therefore, this research is expected to provide a more detailed analysis of mangrove vegetation dynamics, particularly in National Park areas. We use the Enhanced Vegetation Index (EVI) is used as the vegetation index approach, offering greater sensitivity to vegetation density by incorporating the blue band along with the red and near-infrared bands (Huete et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Ramdani et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The findings of this study are expected to provide a more detailed overview of mangrove cover dynamics within National Parks areas, serving as valuable reference for future mangrove ecosystem management.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Area\u003c/h2\u003e\u003cp\u003eThis study was carried out over the entire of Indonesia, with a particular emphasis on National Park regions that are connected to mangrove ecosystems. The boundary of the National Parks was obtained from the World Databased on Protected Areas (WDPA), which is managed by the United Nations Environment Programme World Conservation Monitoring Centre (UNEP-WCMC). The WDPA data can be accesses through the website \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA\u003c/span\u003e\u003cspan address=\"https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (UNEP and IUCN, 2005). During this time, we utilised the National Mangrove Map dataset that was developed by the Indonesian Ministry of Environment and Forestry (Ditjen PDASRH, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This dataset was utilized to collect information regarding the spatial distribution of mangrove ecosystems. This data serves as the major reference map for the distribution of mangrove in Indonesia, which was previously analysed using data from multi-resolution remote sensing satellites. Upon completion of the preliminary identification procedure, it was discovered that twenty-three National Parks contain mangrove habitat and are located in coastal areas.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData\u003c/h3\u003e\n\u003cp\u003eThis study utilizes satellite imagery from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager-Thermal Infrared Sensor (OLI-TIRS), all of which have a spatial resolution of 30 meters. The dataset spans the period from 1995 to 2024, with Landsat 5 covering 1995\u0026ndash;1999, Landsat 7 (2000\u0026ndash;2012), and Landsat 8 (2013\u0026ndash;2024). Landsat is a medium-resolution satellite imagery with a pixel size of 30 meters and 16 days temporal resolution. The dataset used in this study encompasses the entire territory of Indonesia, in accordance with the distribution of National Parks. The data applied are Level 2A products, which have undergone geometric and radiometric corrections to provide surface reflectance values for improved analytical accuracy. Landsat is an optical satellite imagery system whose data quality is significantly influenced by cloud cover, particularly in tropical regions. To address this limitation, an annual median composite approach is applied to the dataset prior to vegetation index calculation, generating a single image per year with minimal cloud contamination. The images selected for compositing have cloud cover of less than 5%. One challenge during compositing is the sensor malfunction in Landsat 7 ETM\u0026thinsp;+\u0026thinsp;that caused the Scan Line Corrector (SLC) failure from 2004 to 2012. This issue is also addressed through the use of annual median compositing, which effectively represents the data for each year. The compositing process involves a maximum of approximately 22 raster images per year using the median statistic due to its robustness in representing the central tendency of time series data at each pixel (Diniz et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). All data processing was conducted using the Google Earth Engine (GEE) platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://code.earthengine.google.com\u003c/span\u003e\u003cspan address=\"https://code.earthengine.google.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a cloud computing system with extensive server capacity for processing data in Earth Sciences. The GEE platform provides the capability to perform analyses on a large spatial scale using multiple satellite imagery data.\u003c/p\u003e\u003cp\u003eTo determine the extent of mangrove areas, we employed two primary data sources: the Global Mangrove Watch (GMW) and National Mangrove Maps (2021) from Indonesian Ministry of Environment and Forestry (KLHK). The National Mangrove Map is a unified dataset that represent the mangrove ecosystem cover across Indonesia and serves as a reference for mangrove cover analysis. This dataset was produced through satellite image analysis using both high and medium-resolution imagery, combined with various classification methods. However, the National Mangrove Maps does not cover all locations, as certain areas have undergone land cover change from mangrove to other land cover types, necessitating integration with additional datasets. Therefore, time series data is essential to capture these changes over time. By incorporating temporal analysis, the pattern of mangrove dynamics throughout the study period can be effectively observed. So, we utilize the overlaid GMW data from 1996 to 2020 combined with the National Mangrove map to produce a single dataset that serves as the Area of Interest (AOI) for mangrove areas. Subsequently, the unified mangrove dataset was clipped using the outer boundary polygon of the National Parks, which were previously obtained from the WDPA. We also use the Global Mangrove Watch (GMW) data to identify the changes of mangroves in each National Parks locations. This data generates from multi temporal Synthetic Aperture Radar (SAR) imagery from 1996\u0026ndash;2020. All the dataset can be accessed freely from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.globalmangrovewatch.org/\u003c/span\u003e\u003cspan address=\"https://www.globalmangrovewatch.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and more discussion about this data can read in the (Bunting et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eMangrove Enhanced Vegetation Index (EVI) Analysis\u003c/h3\u003e\n\u003cp\u003eTo describe the dynamics of the mangrove ecosystem in the NP areas, we applied the statistical trend of Vegetation Index (VI). Where this index indicates the forest cover density based on the greenness values from satellite imagery data. The VI that is used for this research is the Enhanced Vegetation Index (EVI). This index was chosen due to its sensitivity for detecting the forest cover density more than another, as it incorporates the blue channel in its calculation process (Huete et al., \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e; Ramdani et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). The use of the blue band helps to reduce atmospheric interference, which is particularly significant for reflectance values in tropical regions such as Indonesia. The equation for calculating EVI is as follows (Huete et al., \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e):\u003c/p\u003e\n\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:EVI=\\:\\frac{2.5\\:(NIR-Red)}{NIR+6Red-7.5Blue+1}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eWhere NIR, Red, and Blue represent the reflectance values of the wavelength in Band 4, 3, and 1 for Landsat 5 and 7, as well as Band 5, 4, and 2 for Landsat 8. The EVI value ranges from − 1 to 1, where a higher value indicates denser vegetation cover. The annual EVI value is represented as a single spatial value, derived from the median composite over one year. To examine temporal variations in vegetation dynamics across each National Park, we applied a boxplot visualization technique. This method provides a statistical summary of annual EVI values in each National park with highlighting the median, spread, and presence of outliers within the dataset. Annual EVI values from 1996 to 2021 were aggregated for each National Park, allowing for a clear assessment of vegetation trends and fluctuations over time. Boxplots were used to compare variability among parks and to identify areas with consistent or changing vegetation conditions.\u003c/p\u003e\n\u003cp\u003eTemporal trends in EVI were analyses by performing a linear regression at the pixel level, using year as the independent variable and EVI as the dependent variable. The significance of the slope value for each pixel is assessed using the p-value, with a maximum allowable threshold of 0.05 in this study. This means that only slope values with a p-value within the range of -0.05 \u0026lt; p-value \u0026lt; 0.05 are considered representative. Values outside this range are deemed statistically insignificant for depicting the dynamics of mangrove vegetation change. The results of the trend analysis were subsequently classified into two categories, namely Decrease and Increase, based on the slope values derived from the analysis. (Ramdani et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Browning refers to areas with negative slope values, indicating a declining trend, whereas greening corresponds to positive slope values, reflecting an increasing trend. The proportion of mangrove area within the national park (NP) exhibiting either browning or greening trends was then calculated to determine the percentage of the total area represented by each category.\u003c/p\u003e\n\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\n\u003cp\u003e\u003c/p\u003e\n\n\n\n\n\n\n\n\n\n\n\n"},{"header":"3. Result and Discussion","content":"\u003cp\u003e\u003cstrong\u003e3.1. General Pattern of EVI in mangrove National Parks\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eAn overlay analysis of the distribution of mangrove ecosystems and conservation area maps indicates that around 23 National Parks in Indonesia encompass mangrove habitats, spanning an estimated 320,000 hectares (approximately 9.4% of the nation's total mangrove area) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The majority of the National Park including extensive mangrove ecosystems is located in coastal areas adjacent to significant rivers. Two National Parks demonstrate substantial mangrove coverage: Lorentz National Park (LRZ) in Papua, with roughly 195,100.96 hectares (60.58% of its area), and Sembilang National Park (SBG) in Sumatra, with around 86,430.78 hectares of mangrove (26.83%). Conversely, the other 21 National Parks individually account for less than 3% of the overall mangrove coverage throughout all National Park regions. Extensive mangrove habitats are generally located in national parks along significant river estuaries, where the merging of freshwater and seawater, coupled with substantial sediment deposition, fosters optimal circumstances for mangrove growth. Nevertheless, mangrove ecosystems have also developed significantly in areas with relatively low sediment deposition, such as island regions. For instance, the Togean Islands (KTG) rank fourth in terms of mangrove ecosystem area within National Parks, covering approximately 6,315.18 hectares. Although this represents a relatively small percentage, it is noteworthy when compared to mangrove ecosystems located in riverine or estuarine regions, which typically exhibit higher levels of sediment accumulation. Next, several National Parks support relatively small mangrove areas, including Berbak (BRB) with 64.96 hectares, Siberut (SBR) with 132.67 hectares, and Manusela (MNS) with 288.31 hectares. In general, mangrove areas with limited spatial extent are typically found in coastal zones characterized by sandy or rocky substrates with minimal mud deposition. Other parks also exhibit limited mangrove coverage, highlighting variability in the spatial distribution of mangrove ecosystems across the National Park network. For the complete data of the mangrove area coverages in each National Park can be seen in the Supplementary Material 1.\u003c/p\u003e\u003cp\u003eThe analysis of Enhanced Vegetation Index (EVI) values over a 30-year period (1995–2024) across 23 national parks in Indonesia reveals substantial spatial variability in vegetation coverages and condition. Across all National Park locations, the average EVI value specifically within mangrove areas was found to be 0.44 ± 0.08 based on 30 years of temporal data. Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the boxplot illustrating the temporal variation of EVI throughout the entire study period in each National Park. This value indicates that mangrove ecosystem coverage in National Park areas falls within the moderate category. Among all sites, Berbak (BRB) and Sembilang (SBG) exhibited the highest mean EVI values, at 0.60 and 0.54, respectively. These were closely followed by Gunung Palung National Park (GNP), with a mean EVI of 0.52. These three areas also exhibited relatively high upper quartile (Q3) values, suggesting that much of the EVI data consistently exceeded 0.5, reflecting dense and healthy vegetation cover throughout the study period. Conversely, Kepulauan Seribu (KPS), a small island ecosystem located in Jakarta Bay, recorded the lowest mean EVI (0.2145), with observed values ranging between 0.1398 and 0.3219. Similarly, Komodo (KMD) (0.2934) and Bali Barat (0.3548) also exhibited low average EVI values. These results are indicative of their naturally arid environmental conditions and more open vegetation structures, typical of the drier eastern Indonesian archipelago. National parks located in Sulawesi and Papua, such as Wakatobi (WKT), Rawa Aopa Watumohai (RAW), and Wasur (WSR), displayed moderate mean EVI values, ranging from 0.4296 to 0.4504. These areas also exhibited notable variability in vegetation conditions, as indicated by the wider interquartile ranges (IQR) between the 25th (Q1) and 75th (Q3) percentiles. In particular, parks such as Manusela (MNS) and Kutai (KTI) showed considerable EVI spread, suggesting the presence of temporal or spatial heterogeneity likely driven by seasonal changes, mixed vegetation types, and potential anthropogenic disturbances.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e3.2. Trend in EVI Mangroves of National Park\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe EVI trend analysis was conducted both spatially and by averaging all pixel values within each National Park. The average trend of EVI changes for each National Park is presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, showing the percentage of areas experiencing an increasing or decreasing trend. Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e displays the spatial trend analysis results for selected National Parks with the largest mangrove areas, namely Lorentz (LRZ), Sembilang (SBG), Wasur (WSR), and Kepulauan Togean (KTG). The spatial presentation of the analysis results has undergone a masking process in which only pixel trend values with a p-value less than 0.05 (95% confidence level) are shown. Among the four National Parks with the largest mangrove extent, the distribution of EVI trend values is quite varied. In the LRZ area, most pixels exhibit relatively small trend values, ranging between 0.00 and 0.005. This range indicates that changes in mangrove cover are relatively minor. Some specific points, especially in coastal areas, appear greener, indicating trend values greater than 0.01, which means more significant mangrove growth. However, in several locations—both along the coastline and in inner river estuaries—red and orange colors appear, indicating negative trend values (\u0026lt; 0.00). The analysis results show that 92.918% of the mangrove area in LRZ experienced an increase in EVI, while the remaining 7.082% experienced a decrease (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe next National Park with the largest mangrove area is Sembilang (SBG). The EVI trend analysis in this region reveals that several inland mangrove areas, located relatively far from the coastline, tend to exhibit an increasing vegetation index, with trend values ranging from 0.005 to 0.01 (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb). In contrast, areas showing a decreasing EVI trend are predominantly located along the coastal fringe. The proportion of mangrove areas in SBG experiencing an increasing EVI trend is 95.090%, while 4.910% of the area shows a decreasing trend. A similar pattern is observed in Wasur National Park (WSR), where the majority of areas with decreasing EVI trends are concentrated along the coastal mangrove zones, accounting for 9.651% of the total mangrove coverage (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec). The remaining 90.349% of mangrove areas, predominantly located further inland, exhibit increasing EVI trends. The fourth National Park with extensive mangrove cover is the Togean Islands (KTG). In this region, the increase in EVI is substantial and widespread, with 94.231% of the area showing positive trends, while only 5.769% exhibits a decline in EVI values. It is important to note that the KTG region has distinct characteristics, being a karst archipelago with minimal riverine input.\u003c/p\u003e\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the results of the trend analysis of the annual mean values of the Enhanced Vegetation Index (EVI), along with the R\u003csup\u003e2\u003c/sup\u003e (R-Squared) values for each National Park. The table also includes the percentage of mangrove areas that experienced either an increase or a decrease in EVI over the study period. Overall, the avegare EVI values in mangrove areas show an increasing trend across all National Parks. The mean EVI trend for mangrove regions throughout the National Park network is 0.0025 ± 0.001 per year. The analysis reveals that 90.48% of mangrove areas within National Parks experienced an increase in EVI, indicating overall improvements in vegetation condition, while the remaining 9.52% of the area experienced a decrease. The highest average increase in EVI is observed in Kepulauan Seribu (KPS), with a rate of 0.0061 per year. This is followed by Manusela (MNS), Wakatobi (WKT), Alas Purwo (APW), and Kepulauan Karimunjawa (KRJ), each exhibiting an average annual increase of 0.0039. This parks are indicative of strong positive trends in mangrove vegetation greeneess and density. In contrast, the smallest increses in EVI were recorded in Way Kambas (WKB) and Ujung Kulon (UJK) with trend values of only 0.001 per year, suggesting relatively stagnant or slower vegetation recovery in these areas. The R\u003csup\u003e2\u003c/sup\u003e values in this study show different levels of confidence in the linear trend models, ranging from 0.206 (UJK) to 0.88 (MNS). While lower R\u003csup\u003e2\u003c/sup\u003e suggest more variability or noise in the data overtime and higher R\u003csup\u003e2\u003c/sup\u003e show stronger temporal consistency in EVI change. To better illustrate the trend patterns for each National Park, individual EVI trend graphs are provided in Supplementary Material 2.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTrend of EVI in Mangroves Area, Percentages area of increase (decrease) with significant trend over the period of 1995–2024.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eNational Park\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eSlope of EVI Annual Mean Mangrove Area\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003ePercentages (%) Area of Increase EVI\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003ePercentages (%) Area of Decrease EVI\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eLorentz (LRZ)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0019\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e92.918%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e7.082%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6485\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eSembilang (SBG)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0024\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e95.090%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e4.910%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4576\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eWasur (WSR)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0026\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e90.349%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e9.651%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4184\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eKepulauan Togean (KTG)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0026\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e94.231%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e5.769%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7908\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eRawa Aopa Watumohai (RAW)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0022\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e88.572%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e11.428%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6592\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eKutai (KTI)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0017\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e81.874%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e18.126%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3326\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eGunung Maras (GMS)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0016\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e90.156%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e9.844%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4791\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eTanjung Puting (TJP)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0014\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e83.587%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e16.413%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3896\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eTeluk Cenderawasih (TLC)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0021\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e88.967%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e11.033%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8136\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eUjung Kulon (UJK)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0010\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e80.676%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e19.324%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2061\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eBunaken (BNK)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0035\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e94.193%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e5.807%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6393\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eKomodo (KMD)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0016\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e80.767%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e19.233%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4541\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eWay Kambas (WKB)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0011\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e81.073%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e18.927%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2661\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eAlas Purwo (APW)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0039\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e98.429%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.571%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6736\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eWakatobi (WKT)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0039\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e96.496%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e3.504%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7753\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eBali Barat (BLB)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0038\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e97.441%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e2.559%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8044\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eKepulauan Karimunjawa (KRJ)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0039\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e96.038%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e3.962%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7459\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eGunung Palung (GNP)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0021\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e97.021%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e2.979%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eBaluran (BLR)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0025\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e90.694%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e9.306%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6338\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eManusela (MNS)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0039\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e89.057%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e10.943%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8815\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eSiberut (SBR)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0021\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e84.010%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e15.990%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5355\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eBerbak (BRB)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0031\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e93.634%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e6.366%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2416\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eKepulauan Seribu (KPS)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0061\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e95.667%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e4.333%\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8475\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\n\u003c/div\u003e\u003cp\u003eThe ranking of NP is based on the extent of mangrove area. All trend values have a p-value of less than 0.05. A negative (-) or positive (+) value indicates a decrease or increase in mangrove area from 1996 to 2020, respectively. The notation \"NaN\" means that mangroves in the area were not detected based on GMW data.\u003c/p\u003e\u003cp\u003eNearly all National Parks have a largely positive EVI trend across all sites, with variations in the amount of changes and the extent of area impacted. Although most mangrove regions show a rise in EVI, few parks display significant sections experiencing reduction. For example, while KTS and KMD exhibited favorable EVI trends, they also reveal significant proportions of mangrove area with declining EVI (18.13% and 19.29%, respectively) (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Likewise, WKB and UJK observed reductions of 18.93% and 19.32% in their mangrove areas, which similarly exhibited declining EVI. These data may suggest localized disturbances or degrading pressures impacting segments of the ecosystem, notwithstanding the overall favorable trend. Conversely, some National Parks, including APW, BLB, and KRJ, have over 95% of their mangrove areas undergoing rises in EVI, underscoring regions of significant vegetation enhancement. This variety highlights the necessity for tailored monitoring and management techniques to address regions experiencing a decline in vegetation health.\u003c/p\u003e\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates the regional distribution of various National Parks that demonstrate the most significant reductions in EVI. Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea illustrates UJK, situated in the western region of Java Island. The mangrove ecosystems in UJK comprise two primary regions: Java Island and Panaitan Island. The majority of the declining EVI trends (red regions) are focused on the eastern side of Panaitan Island, with smaller patches noted at the tip of Java Island. Conversely, the mangroves on the Java Island side, forming the core zone of UJK, exhibit relative stability, with certain regions displaying a favourable vegetation index trend (green patches). The following area is KMD (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb), an archipelagic zone distinguished by high tourism activity and extensive development. A significant part of mangrove habitats exhibiting decreasing EVI trends is found on the western side of Komodo Island, with lesser impacted regions on Padar and Rinca Islands. Numerous smaller islands within KMD also demonstrate declining EVI patterns.\u003c/p\u003e\u003cp\u003eKTI (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ec) is situated in the eastern region of Kalimantan, specifically inside East Kalimantan Province. In this region, the majority of mangrove areas exhibiting a drop in EVI are located in the northern part of KTI, frequently near settlements. The red areas in KTI are primarily located inland inside the mangrove ecosystem, distant from the coastline, suggesting that deterioration is mainly influenced by terrestrial factors. In contrast, mangroves situated along the coastline exhibit a rising EVI trend. WKB is situated in Lampung Province on Sumatra Island. The mangrove ecosystems in this area are very limited, primarily situated along the eastern coastline. Certain regions demonstrating a drop in EVI are located in the southern section of WKB, specifically inside mangrove zones that extend into the sea (headland areas). Concurrently, other mangrove regions within WKB often exhibit an ascending EVI trend.\u003c/p\u003e\u003cp\u003eThe Global Mangrove Watch (GMW) data was utilized to analyze the alterations in mangrove cover area from 1996 to 2020 (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). This enabled us to assess both the increase and decrease in EVI values. The study's findings indicate that, of the twenty-three National Park sites, over half experienced a decline in mangrove coverage, and the other sites demonstrated an increase. During this period, there was a total reduction of 839.41 hectares in mangrove cover across the National Parks. KMD (-11.79%), KTI (-6.55%), and MNS (-7.75%) incurred the most losses. The statistics given herein corroborate previous findings, which identified KMD and KTI as the national parks that had the most substantial declines in EVI values. In contrast, APW and BLB recorded the most significant increases in mangrove cover, with increased cover of 17.15 and 10.09 percent, respectively. It was determined that both locations are situated along the same coastal region adjacent to the Bali Strait. Moreover, it was shown that 97% of their entire area is demonstrating rising EVI trends, representing the highest proportion among all national parks. Analysis of the region revealed that SBG experienced the most substantial decline in mangrove cover throughout the evaluation period, totaling − 989.75 ha, followed by KTI at -260.04 ha, and RAW at -150.621 ha. Nevertheless, the proportion of mangrove acreage in these locations may be rather insignificant compared to the total mangrove area within the National Park. Concurrently, LRZ experienced the most substantial increase in mangrove area, amounting to 569.70 hectares, followed by APW at 144.14 hectares. For further details concerning the area alterations, please refer to Supplementary Material 3.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe spatial distribution of mangrove habitats within Indonesia's National Parks covers nearly all regions, whether they are large or small islands. Mangroves, which flourish in tropical climates, are primarily located in national parks that feature vast swamps and significant river estuaries. This pattern is illustrated by three National Parks: LRZ, SBG, and WSR, which host the most extensive mangrove ecosystems in Indonesia, all located within vast estuarine and swamp-dominated lowland areas. It is important to highlight that LRZ and WSR are located in the southern lowland swamps of Papua Island, an area recognised as one of the largest wetland regions in Indonesia (Jia et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These areas serve as major sediment deposition zones that have accumulated over long geological periods, supporting the development of some of the most expansive mangrove ecosystems in Indonesia (Brunskill et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Setyadi et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In contrast, SBG is located within the estuarine systems of the Musi and Batanghari rivers along the eastern coast of Sumatra (Purwanto et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Mangroves necessitate particular environmental conditions, including suitable substrate types like mud or sand, along with exposure to seawater salinity to facilitate their growth and reproduction (Boubakary et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; R. S. Costa et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Estuarine environments are particularly suitable for the development of mangroves because of their favourable substrates and steady supply of organic matter, which is essential for the physiological processes that support mangrove metabolism (Li et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sarker et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn addition to the extensive mangrove ecosystems found in estuarine lowlands. Several National Parks in Indonesia also host mangrove communities within island and karstic environment, which smaller in spatial extent and ecologically significant and represent a distinct mangrove biogeographic setting. A notable example is KTG in Central Sulawesi, where mangroves cover approximately 6,315.28 ha, thriving on carbonates substrates and limestone dominated coastal zones (Rositasari et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sendjaja et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similarly with KMD in East Nusa Tenggara and MNS on Seram Island (Maluku Province) also feature island based mangrove situated in volcanic or karst landscapes with limited freshwater inflow and minimal sediment depositions (Cort\u0026eacute;s-Esquivel et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Henri et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Terzić et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These area are generally characterized by higher salinity levels, shallow soils, and steeper coastal gradients that all of which impose ecological constraints that select for salt tolerant and stress adapted mangrove species (Godoy et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Medina-Calder\u0026oacute;n et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The mangrove species that dominate such as environments often include \u003cem\u003eSonneratia sp\u003c/em\u003e, \u003cem\u003eAvicennia sp\u003c/em\u003e, and other taxa with specialized adaptations such as pneumatophores, salt excreting leaves, and robust root systems capable of anchoring in rocky or compacted substrates (Feng et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hao et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although species richness in these island system may be lower compared to those in nutrient rich estuarine, they perform critical ecological functions, including shoreline stabilization, sediment trapping, and serving as nurseries for another biotas around mangrove ecosystems (Basyuni et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Manna et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBased on satellite-derived assessments, the averages of EVI values of Mangrove Ecosystems across Indonesia\u0026rsquo;s National Parks indicate a moderate level of vegetation density and greenness (Nepita-Villanueva et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ramdani et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This suggest that many mangrove stands exhibit relatively healthy canopy conditions, although spatial variability in vegetation quality remains evident across sites. Notably, The BRB, SBG, and GNP National parks recorded the highest EVI values, reflecting dense canopy structure and generally healthy vegetation status (Ishtiaque et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Samanta et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These favorable conditions are likely influenced by supportive environmental factors such as sustained freshwater input, nutrient enriched estuarine systems, and minimal anthropogenic disturbance. For example, BRB and SBG are located at the mouth of major rivers on the eastern coast of Sumatra (Abdillah et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which enhances nutrient availability and hydrological stability (Forouzannia \u0026amp; Chamani, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Phong \u0026amp; Luom, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, their relative remoteness from human settlements may contribute to reduced anthropogenic pressure, thereby supporting more stable and healthy mangrove ecosystems. Moreover, the KPS, KMD, and BLB National Parks recorded as the lowest mean EVI values, ranging between 0.21 to 0.35. These relatively low values are indicative to naturally arid climate conditions, rocky or sandy substrates, and limited freshwater or sediment inflow (M. Adame et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; M. T. Costa et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These environmental constraints are particularly common in the eastern Indonesian regions and small islands conditions, where mangrove growth tends to be patchy and sensitive to water availability and salinity stress. While the mangrove cover in mainland regions generally exhibits higher density due to favorable growth conditions (Cahyo et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Najamuddin et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe examination of EVI trends spanning three decades indicates a promising pattern throughout Indonesia\u0026rsquo;s National Parks, with around 90.48% of the total mangrove areas showing a positive trend in vegetation greenness. This notable rise indicates that mangrove ecosystems in these protected regions are generally undergoing enhancements in canopy cover and overall vegetation health, a favourable trend driven by a variety of ecological and human-induced factors. The continuous conservation initiatives, especially within effectively managed national parks, could have promoted the natural regeneration and restoration of degraded mangrove areas. Furthermore, the extensive mangrove areas, including LRZ, SBG, and WSR, are situated in remote or sparsely populated regions, resulting in minimal human-induced disturbances. This condition facilitates the functioning of ecological processes with minimal external influences. The enduring legal protection status of these regions likely contributed significantly to the reduction of deforestation, illegal logging, and land conversion, thus fostering stable or rising vegetation trends (Bonannella et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In various inland and estuarine mangrove areas, observed positive trends could indicate sediment accretion, hydrological stability, and suitable salinity conditions, which are essential environmental factors for promoting mangrove growth. In Sembilang National Park (SBG), the expansion of mangroves along the Musi River estuary is facilitated by ongoing sediment deposition and stable hydrodynamic conditions, which create an ideal substrate for regeneration. In Berbak National Park (BRB), the mangrove areas adjacent to the Batanghari River delta show encouraging trends. The combination of sediment input from upstream and estuarine nutrient dynamics has played a significant role in supporting and promoting mangrove growth in these regions (Prasad \u0026amp; Ramanathan, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; W\u0026ouml;sten et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNotwithstanding the generally favourable trend of mangrove greenness in Indonesia\u0026rsquo;s National Parks, specific localised regions continue to demonstrate decreasing EVI trends, especially along coastal margins and inside small island ecosystems. The KMD National Park has exhibited significant negative EVI trends in particular areas. This is linked to its designation as one of Indonesia\u0026rsquo;s Super Priority Tourism Destinations, attracting substantial annual visitor numbers (Hidyarko et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mahmudin et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Miftah Wirakusuma et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Mangrove ecosystems in KMD are frequently deprioritized in conservation efforts relative to tourism development, leading to increased ecological stresses and insufficient conservation focus (Macamo et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A similar situation can be seen in KPS National Park, which is located in the urban region of Jakarta. There are considerable portions that have a falling EVI. As the marine and coastal protection area that is geographically closest to the capital, the KPS is subject to a high amount of anthropogenic stressors (Setiawati et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yonvitner et al., 2022). These stressors include urban expansion, industrial discharges, coastal reclamation, and extensive fishing operations. All of these stressors place a tremendous amount of strain on the mangrove ecosystems that are located inside the KPS. Simultaneously, in UJK National Park, the emergence of localised adverse trends underscores the susceptibility of mangroves located near human activities, shipping lanes, and coastal communities. The findings indicate that although national conservation efforts have successfully preserved favourable mangrove conditions, localised management challenges persist in areas where tourism development, urbanisation, and direct human pressures intersect with mangrove ecosystems.\u003c/p\u003e\u003cp\u003eThe investigation indicated that KMD, KTI, UJK, and WKB are the regions exhibiting the most pronounced decrease in EVI. This trend aligns with the Global Mangrove Watch Data indicating a reduction in mangrove area from 1996 to 2020, signifying both functional and physiological deterioration of mangrove ecosystems. The decline of mangrove in KMD is mostly attributable to proliferation of tourist, coastal development project, and unsustainable activities. The distinctive geographical features of these islands, including particular geomorphological development and restricted freshwater runoff, can affect the natural regeneration and dynamics of mangrove ecosystems (Debrot et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Fujimoto, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), whereas the increasing demands for tourism infrastructure have markedly expedited habitat conversion (Duvat et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Majiid et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rimba et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Waleed et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Yusuf et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The mangroves in KTI are detached from terrestrial ecosystems, rendering them more susceptible to the encroachment of human developments and direct anthropogenic influences (Blanco-Libreros \u0026amp; Ram\u0026iacute;rez-Ruiz, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Panggabean et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The observed decline in EVI in this region primarily occurred around coastal communities, signifying significant anthropogenic impacts (Gon\u0026ccedil;alves et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This conversion of mangrove ecosystems into agricultural or aquaculture land is prevalent in Indonesia, particularly in Kalimantan Island, which is significantly affected, along with the KTI region (Ilman et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eConversely, Ujung Kulon (UJK) and Way Kambas (WKB) demonstrated a reduction in EVI mostly in coastal regions distant from direct human settlement, indicating that climate change and coastal erosion are the principal contributing variables. The southern waters of Java are notably susceptible to severe wave activity, particularly in the Sunda Strait region. This region is subject to significant risk of coastal erosion along its coastlines. Elevated sea levels, coastal erosion, and enhanced hydrodynamic activities are probabl1e factors in the noted deterioration. This pattern demonstrates a dual dynamic: manmade drivers, such as land conversion and tourism, are more significant in KMD and KTI, whereas natural drivers associated with climate variability and coastal processes exert a higher influence in UJK and WKB. These findings underscore the necessity for conservation strategies customized to local circumstances, balancing the regulation of human activities with the implementation of adaptive measures to alleviate the effects of climate change. Moreover, the susceptibility of these coastal ecosystems is exacerbated by their restricted natural recuperative ability, especially in regions experiencing substantial human impacts or pronounced geomorphological alterations.\u003c/p\u003e\u003cp\u003eThe management of national parks in Indonesia is now conducted directly by the Ministry of Forestry. The overlay study indicates that 23 national parks contain mangrove forests; nevertheless, only three of these parks (LRZ, SBG, and GMS) had mangrove regions exceeding 10% of their overall area. Most other parks have a mangrove fraction below 5%, often including lowland forest regions with coasts that include mangrove habitats. As of yet, no National Park in Indonesia has been established as a management area exclusively for mangrove ecosystems. The majority of mangrove forests in Indonesia are classified as Protected Forest and Production Forest (Ilman et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The distribution of mangroves inside National Parks may be classified into two primary categories: those situated in vast river estuaries and those found on tiny islands. In mangrove regions located in extensive river estuaries, substantial large-scale alterations in mangrove coverage inside National Parks have often not transpired. Only a limited number of parks, including SBG and KTI, have seen significant alterations due to land use conversion and forest fires (Dwiyahreni et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Guild et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThree decades of EVI trends have given us some information, but we still need to perform more research to fully understand how mangroves work in Indonesia's Conservation Management Areas. To better understand the connection between canopy greenness and ecosystem performance, future research should include both satellite-derived vegetation indices and field estimates of biomass, carbon storage, and species composition. To make conservation efforts fairer, we need to look at social and biological factors, such as how much the local community depends on mangroves and how tourism and aquaculture affect them. Predictive modelling can look at how mangrove ecosystems are affected by climate change, hydrodynamic processes, and changes in land use to see how vulnerable and strong they are. In the future, researchers may use remote sensing, ecological monitoring, and socio-economic frameworks to help set conservation goals and adapt management in Indonesia's National Parks, which are known as important Management Areas of Conservation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis research was finding the spatio-temporal dynamics of mangrove vegetation health in Indonesia’s National Parks through the use of the Enhanced Vegetation Index (EVI) for the years 1995–2024. The mean EVI for all National Parks was 0.44, signifying that mangroves are typically in moderate condition. Over 80% of mangrove regions exhibited a rising EVI trend, indicating a general increase in ecosystem vitality. The APW, BLB, and GNP regions demonstrated the largest percentage of rising EVI values, with APW further noting the most significant increase in mangrove areas and the highest average EVI. KMD and KTI had the highest percentage of diminishing EVI trends, which aligned with decreases in mangrove coverage. The decreases were mostly linked to human-induced disruptions, including land-use changes, forest fire and unsustainable tourism practices. The overall trends demonstrate that National Parks significantly contribute to the preservation of mangrove ecosystems, as evidenced by the comparatively fewer negative trajectories relative to favourable ones. This finding indicates that the protected area concept has yielded quantifiable conservation advantages for mangroves, although localised pressures still pose a substantial issue. Considering that Indonesia currently lacks a national park specifically focused on mangrove protection, the creation of such protected areas could enhance national conservation initiatives. A future study must include evaluations of ecosystem services, such as carbon sequestration, and utilise dynamic system modelling to facilitate adaptive management and ensure the long-term viability of mangrove ecosystems in national parks.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the Ministry of Higher Education, Research, and Technology of the Republic of Indonesia for providing funding for this research and publication through the Fundamental Research Regular scheme, fiscal year 2025, under Contract Number B/041/UN46.1/PT.01.03/BIMA/PL/2025.\u003c/p\u003e\n\u003cp\u003eEthics and Consent to Participate declarations: \u003cstrong\u003enot applicable\u003c/strong\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eH.A.R : Conceptualization, Writing-Original Draft, Investigation, Formal Analysis, Methodology, Visualization; Z.H: Investigation, Review and Editing; A.R.A : Conceptualization, Investigation, Formal Analysis, Review and Editing; M.K.W : Review and Editing, Funding Acquisition; All author reviewed the manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdillah, M., Utomo, B., \u0026amp; Sulistioadi, J. 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Coastal protection by planted mangrove forest during typhoon mangkhut. \u003cem\u003eJournal of Marine Science and Engineering\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(9), 1288.\u003c/li\u003e\n\u003cli\u003eUNEP-WCMC and IUCN (2025), Protected Planet: The World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM) [Online], January 2025, Cambridge, UK: UNEP-WCMC and IUCN.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mangrove, National Parks, Enhanced Vegetation Index (EVI), Satellite Imagery, Remote Sensing Data","lastPublishedDoi":"10.21203/rs.3.rs-7554686/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7554686/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMangroves are coastal ecosystems essential for coastline protection, biodiversity preservation, and carbon sequestration and storage. A primary strategy for mangrove preservation in Indonesia is the establishment of protected zones, including National Parks. Monitoring mangrove ecosystems over extensive spatial areas necessitates sophisticated methodologies, including the utilisation of satellite-based remote sensing data. This study examines the dynamics of mangrove cover change in all National Parks in Indonesia utilising the Enhanced Vegetation Index (EVI) obtained from Remote Sensing Imagery from 1995 to 2024. We used the Landsat Imagery from Landsat 5 TM (1995 to 2000), Landsat 7 ETM+ (2000 to 2012), and Landsat 8 OLI-TIRS (2013 to 2024). The analysis includes 23 National Parks featuring coastal mangrove ecosystems. Lorentz (LRZ) and Sembilang (SBG) National Parks encompass the most extensive mangrove regions, predominantly situated in significant river estuaries that offer optimal conditions for mangrove proliferation. Mangrove ecosystems in National Parks located on larger islands generally display elevated temporal mean EVI values relative to those on smaller islands. Significant reductions in EVI were noted in Ujung Kulon (UJK), Komodo (KMD), Kutai (KTI), and Way Kambas (WKB) National Parks, while Alas Purwo (APW) exhibited the least loss, indicating comparatively well-preserved mangrove conditions. Trend analysis indicated that over 80% of mangrove regions within each National Park displayed favourable EVI trends, signifying enhancements in mangrove ecological conditions. In contrast, localised regions exhibiting negative trends were predominantly linked to heavy tourist and land conversion operations, as noted in KMD and KTI. Conversely, National Parks situated in more isolated areas, like APW, exhibited constant positive EVI trends, correlating with substantial mangrove area expansion. The mangrove ecosystem cover within National Parks is generally still well preserved, with no extreme changes observed. Several factors, such as tourism activities, forest fires, and land-use conversion, are the main drivers of mangrove cover decline in certain locations. The results of this study are expected to serve as a reference for future mangrove ecosystem management, particularly within the framework of National Park\u0026ndash;based conservation.\u003c/p\u003e","manuscriptTitle":"Long-Term Dynamics of Mangrove Vegetation Coverages in Indonesia’s National Parks Derived from Remote Sensing Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-16 16:13:56","doi":"10.21203/rs.3.rs-7554686/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1798414c-7210-4c20-a1bc-c71a603c6a95","owner":[],"postedDate":"October 16th, 2025","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-19T21:52:14+00:00","index":88,"fulltext":""},{"type":"reviewerAgreed","content":"152068605945303759930931677139044928803","date":"2026-05-07T02:36:00+00:00","index":85,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-16T16:13:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-16 16:13:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7554686","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7554686","identity":"rs-7554686","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}