Spatio-Temporal Changes in Mangroves in Sri Lanka: Landsat Analysis from 1987 to 2022

Abstract

Mangroves in Sri Lanka provide critical ecosystem services, yet they have undergone significant changes due to anthropogenic and natural drivers. This study presents the first national-scale assessment of mangrove dynamics in Sri Lanka using remote sensing techniques. A total of 4670 Landsat images from Landsat 5, 7, 8, and 9 were selected to detect mangrove distribution, changes in extent, and structure and stability patterns from 1987 to 2022. A Random Forest classification model was applied to elucidate the spatial changes in mangrove distribution in Sri Lanka. Using national-scale data enhanced mapping accuracy by incorporating region-specific spectral and ecological characteristics. The average overall accuracy of the maps was over 96.29%. The total extent of mangroves in 2022 was 16,615 ha, representing 0.25% of the total land of Sri Lanka. The results further indicate that, at the national scale, mangrove extent increased from 1989 to 2022, with a net gain of 1988 ha (13.6%), suggesting a sustained and continuous recovery of mangroves. Provincial-wise assessments reveal that the Eastern and Northern Provinces showed the largest mangrove extents in Sri Lanka. In contrast, the Colombo, Gampaha, and Kalutara districts in the Western Province showed persistent declines. The top mangrove spatial structure and stability districts were Jaffna, Trincomalee, and Gampaha, while the most degraded mangrove districts were Batticaloa, Colombo, and Kalutara. This study offers critical insights into sustainable mangrove management, policy implementation, and climate resilience strategies in Sri Lanka.

1. Introduction

Mangroves primarily consist of salt-tolerant plants in natural transitions between coastal marine environments and dry terrestrial ecosystems [1,2,3]. They are important to humanity, providing numerous ecosystem services such as carbon sequestration, coastal protection, fisheries, biodiversity, water purification, sediment stabilization, livelihoods, and economic benefits [4,5,6]. Mangroves also play an essential role in the Sustainable Development Goals (SDGs), especially SDG 1: End poverty in all its forms everywhere; SDG 13: Take urgent action to combat climate change and its impacts; SDG 14: Conserve and sustainably use the oceans, seas, and marine resources for sustainable development; and SDG 15: Life on Land [7]. However, increasing anthropogenic activities [8,9,10] and the growing pressure of climate change influence mangroves [11,12,13] and their combined ecosystem services in numerous ways [14,15,16,17].

Many studies have reported mangrove losses [18,19,20]. However, mangroves can respond to environmental changes at local and regional scales, increasing their extent via physical propagation—sedimentation, encroachment, and segmentation [21,22]. Large-scale mangrove rehabilitation activities further propagate and recover these natural processes [23,24]. Previous studies have reported that mangroves are particularly vulnerable to ecological and anthropogenic change [11,25,26]; however, our understanding of their impact on mangroves remains unclear.

Today, mangroves are recognized as one of the most vulnerable ecosystems and have been affected by two main processes: land-direction and sea-direction processes [27,28]. Land-direction processes include human activities, hydrological alteration, and tidal flooding hampers such as dikes and roadways [2,27]. Accelerated sea-level rise, strong storm waves, tsunamis, and other climate phenomena are examples of sea-level processes [2,27,28]. Impacted mangrove forests show reduced ecosystem services [5,20,29,30], potentially modifying their role in the global carbon cycle from a carbon sink to a carbon source [11,31,32]. Therefore, detecting mangrove changes is crucial for restoring and enhancing carbon sequestration, as well as supporting national and international climate initiatives.

Mangrove change detection is required to identify their future trajectories. Generally, traditional surveying methods are challenging to apply in mangrove detection due to the large physical environment, and these techniques are often used for relatively small areas over a short period [33,34]. Furthermore, due to the challenges of processing large quantities of Earth observation data, traditional mangrove detection systems generally use fewer images at a particular time [35,36]. Conversely, the continuous advancement of state-of-the-art technology (remote sensing) and the increasing availability and accessibility of the data offer a significant opportunity for the spatio-temporal monitoring of large-scale and long-term changes in mangroves [37,38]. For example, Global Mangrove Watch (GMW) aims to provide comprehensive, up-to-date data on the distribution, status, and changes in mangroves worldwide [39]. The GMW provides valuable insights at the global level; however, direct application of data at the regional and national levels remains challenging [40]. The Google Earth Engine (GEE) cloud-based data computing system has emerged as a prompt and suitable solution to this problem [41].

Previous studies have revealed patterns of mangrove loss and gain due to coastal development, aquaculture expansion, climate change, and conservation efforts worldwide [42,43]. These changes are not consistent, with distinct trends often observed between the Global North and the Global South [44,45], as well as between tropical and subtropical regions [46,47]. For instance, some areas in the Global North exhibit stable or expanding mangrove cover due to protection and restoration initiatives [44]. Conversely, many regions in the Global South, particularly Latin America, Southeast Asia, and South Asia, have experienced substantial degradation of their mangrove forests. Tropical mangroves include the majority of the global mangrove area and are especially vulnerable to anthropogenic pressures and sea-level rise. For example, extensive deforestation is observed in the Mekong Delta [48], the Caribbean mangroves [49], and in more localized yet significant areas, such as parts of West Africa, where clearing occurs for urban expansion [50]. Sri Lanka, situated in the tropical zone near the equator, possesses significant mangroves that are subject to similar pressures, including encroachment for aquaculture, agriculture, coastal infrastructure development, urbanization, pollution, and climate impacts, making it a crucial area for focused mangrove change detection studies to inform national conservation and management strategies [51].

Sri Lanka stretches between 6° N and 10° N latitude and 79° E and 82° E longitude in the Indian Ocean—the total area of the country is approximately 65,610 km² [52]. The coastline stretches approximately 1340 km. The island features a mountainous area in the southern and central regions, as well as a surrounding coastal plain with rich ecosystems, including mangroves. Mangroves in Sri Lanka are distributed across fourteen coastal districts in five provinces (Table S1). The wet and dry climate conditions of Sri Lanka are favorable for propagating mangroves [53]. Sri Lanka is particularly vulnerable to climate change due to its high precipitation and temperatures, complex hydrological cycle, and exposure to catastrophic weather events, especially cyclonic activities from the Bay of Bengal and the Arabian Sea [53].

Figure 1 shows the selected locations of mangroves along the coastal areas of Sri Lanka. Sri Lanka is home to six Ramsar wetlands, recognized globally for their exceptional biological and ecological significance [54]. Moreover, due to its rich wetland ecology, the capital, Colombo, was declared a Ramsar wetland city by the Ramsar in 2018 [54,55].

Despite having a complex mangrove ecology, it is surprising that there are no high-resolution, high-accuracy, and long-term mangrove distribution data to track mangrove dynamics in Sri Lanka. The International Union for Conservation of Nature (IUCN) Sri Lanka and the Central Environmental Authority (CEA) of Sri Lanka published the national wetland directory in 2006 [51]. However, there has yet to be a concrete program to monitor wetland changes in the country, including those of mangroves. Many studies have focused on wetland mapping and change over the past decades in Sri Lanka [57,58,59]. However, these studies are considered relatively small in terms of wetland areas [58,59,60]. Therefore, an overall view of the mangroves of Sri Lanka is currently needed to understand past and present changes and their future trajectories.

Many studies have demonstrated the significance of analyzing mangrove spatial structure, fragmentation, and overall configuration for sustainable mangrove conservation, enhancing restoration efforts, and improving ecosystem services [37,57,61]. However, in Sri Lanka, few studies have systematically addressed the structural and stability patterns of mangroves [57]. Therefore, a fundamental assessment is required to detect changes in spatial structure, fragmentation, and stability patterns, thereby improving core habitat integrity and ecosystem services across the entire country of Sri Lanka. Addressing this gap is crucial for developing evidence-based conservation strategies, enhancing ecosystem restoration efforts, and strengthening Sri Lanka’s contributions to global climate mitigation. Therefore, our study area is defined by creating a coastal buffer that extends 10 km into the sea and the land area from the coastline of Sri Lanka (representing the extent of mangroves in Sri Lanka), because mangroves typically occur within low-lying coastal zones influenced by tidal action, salinity, and sedimentation (Figure 2).

This study aims to (i) detect mangrove distribution in Sri Lanka from 1987 to 2022, utilizing available Landsat imagery to ensure long-term temporal consistency and spatial coverage across the entire country, using the national-scale data by incorporating region-specific spectral and ecological characteristics, enabling a robust assessment of mangrove distribution and dynamics; (ii) monitor and analyze mangrove gain and loss in different scales, including national, provincial, district levels, and climatic zones; and (iii) analyze the structure and stability patterns of mangroves in Sri Lanka, emphasizing whether they are static or changing spatiotemporally over time. The results of this study can provide theoretical guidelines for future mangrove research and the sustainable management of mangroves in Sri Lanka. Scheme 1 shows the selected locations of mangroves along the coastal areas of Sri Lanka for visual interpretation.

Figure 2. Sri Lanka and its surrounding environment. Red labels on the map indicate the coastal districts of Sri Lanka. The purple buffer represents the coastal zone used in this study, extending 10 km seaward and 10 km inland. Basemap source: Esri Map [62]. The black labels on the figure indicate the locations shown in Figure 1. The green labels on the figure indicate the locations shown in Scheme 1.

Figure 2. Sri Lanka and its surrounding environment. Red labels on the map indicate the coastal districts of Sri Lanka. The purple buffer represents the coastal zone used in this study, extending 10 km seaward and 10 km inland. Basemap source: Esri Map [62]. The black labels on the figure indicate the locations shown in Figure 1. The green labels on the figure indicate the locations shown in Scheme 1.

2. Materials and Methods

2.1. Typology of Mangrove Mapping

This study used state-of-the-art technology (remote sensing) and other supplementary data to establish a framework for mangrove distribution dynamics and changes in Sri Lanka. In our study, mangroves are considered generally inundated throughout the tide cycle and comply with the definitions of the IUCN Global Ecosystem Typology (Typology code MFT1.2—intertidal forests and shrublands considered mangroves) [63]. Furthermore, we also considered the definition of Sri Lanka’s coastal zone as outlined in the Coastal Conservation Act of Sri Lanka [64]. Then, we developed a classification system based on remote sensing data, supplemented by ancillary data, to detect the extent of mangroves at ten stages from 1987 to 2022, based on the availability of Landsat data (see Section 2.2 and Section 2.3).

2.2. Landsat Images

The Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper+ (ETM+), and Landsat 8 and 9 Operational Land Imager (OLI) are the most critical data sources for detecting long-term and large-scale changes in terrestrial ecosystems [65]. In this study, the Landsat Collection 2 atmospherically corrected surface reflectance data was obtained from the GEE from 1987 to 2022 (4670 images) (Table S2), a cloud and cloud shadow mask was applied using the CF mask algorithm [66], and a median image composite was used for three-yearly periods (1987–1989, 1993–1995, 1996–1998, 2002–2004, 2005–2007, 2008−2010, 2011−2013, 2014−2016, 2017−2019, 2020−2022)—totaling ten mangrove stages. The three years were chosen due to the availability of Landsat data and other ancillary data to identify visible changes in the mangroves, as well as to minimize the influence of extreme events. The mangrove layer is named based on the last year of the three years considered. For example, if 1987, 1988, and 1989 are used, the mangrove layer is named 1989. This approach is applied to all other stages.

Figure 3 shows the Landsat images used in this study. Landsat 8 provided the highest number of images (1843), followed by Landsat 5 (1503) and Landsat 7 (1104), while Landsat 9 contributed the least (220 images) due to its recent launch (Table S2). The historical contributions of Landsat 5 and Landsat 7 play a significant role in the long-term monitoring of mangroves in this study. The three most frequently used years were 2022 (810 images), 2004 (538 images), and 2009 (494 images), emphasizing the critical years of data availability for mangrove detection in Sri Lanka. The 141/55, 142/54, 141/54, 142/53, and 140/55 paths and rows of Landsat made the highest contribution to mangrove mapping in Sri Lanka.

2.3. Mangrove Detection in Sri Lanka

Figure 4 shows the schematic diagram of the study. The methodology encompasses mangrove detection, spatial analysis—including loss and gain change detection—assessment of mangrove occurrence from 1989 to 2022, and evaluation of mangrove structural and stability patterns. Before delineating the study area, we visually interpreted the coastal area using other ancillary data and applied a 10 km buffer along the country’s coastline. Ultimately, the study presents evidence-based recommendations to ensure the long-term sustainability of mangroves in Sri Lanka.

2.3.1. Spectral Indices Used in This Study for the Temporal Band Composite

Spectral indices are mathematical combinations of different band wavelengths that enhance specific image features. Several indices were used for mangrove detection to emphasize vegetation health, moisture content, and water presence. We used the following indices to create the temporal band composite for mangrove detection, as they are relevant to mangrove ecosystems in Sri Lanka [67,68].

Normalized Difference Vegetation Index (NDVI)

NDVI is one of the most established indices for vegetation monitoring. NDVI values range from −1 to 1. The higher values indicate dense and healthier vegetation. In the context of mangroves, their dense canopies generally produce high NDVI values. Therefore, the NDVI is crucial because it provides a baseline measure of vegetation health, helping to distinguish between mangroves and non-vegetated areas [69,70].

$$\mathrm{NDVI}={\displaystyle \frac{\mathrm{NIR}-\mathrm{Red}}{\mathrm{NIR}+\mathrm{Red}}}$$

(1)

where NIR and Red are the reflectance values of NIR and Red of Landsat 5, 7, 8, and 9, respectively.

Normalized Difference Mangrove Index (NDMI)

The NDMI is a spectral index used to identify mangroves. The NDMI is particularly sensitive to moisture content in mangroves. Mangroves are often located in waterlogged areas and intertidal zones. The NDMI can isolate mangroves from other vegetation. The enhanced sensitivity of the NDMI to moisture conditions makes it a valuable spectral index for identifying mangroves [71,72].

$$\mathrm{NDMI}={\displaystyle \frac{\mathrm{SWIR}-\mathrm{Green}}{\mathrm{SWIR}+\mathrm{Green}}}$$

(2)

where SWIR and Green are the reflectance values of SWIR and green bands of Landsat 5, 7, 8, and 9, respectively.

Modified Normalized Difference Water Index (MNDWI)

The MNDWI is used to identify water areas, a critical characteristic for mangrove detection (due to the close relationship between mangroves and aquatic environments). Mangroves thrive in intertidal zones and near estuaries and lagoons—a strong water signal (evident from the MNDWI) supports the separation of vegetation (e.g., mangroves) from water areas. The index is calculated as follows [73,74].

$$\mathrm{MNDWI}={\displaystyle \frac{\mathrm{Green}-\mathrm{SWIR} 1}{\mathrm{Green}+\mathrm{SWIR} 1}}$$

(3)

where Green and SWIR1 are the reflectance values of Green and SWIR1 of Landsat 5, 7, 8, and 9, respectively.

Simple Ratio (SR)

The simple ratio can distinguish mangroves, emphasizing NIR reflectance and low red absorption. The simple ratio is ideal for detecting health assessment and monitoring mangrove changes in the target areas [65].

$$\mathrm{SR}={\displaystyle \frac{\mathrm{NIR}}{\mathrm{RED}}}$$

(4)

where NIR and RED are the reflectance values of NIR and RED of Landsat 5, 7, 8, and 9, respectively.

Green Chlorophyll Vegetation Index (GCVI)

The GCVI is crucial for monitoring mangrove health, biomass, and photosynthetic activity because it can evaluate stress conditions, degradation, and restoration of mangroves. The GCVI is sensitive to the chlorophyll content in vegetation and is an effective indicator of plant health. High GCVI values indicate vigorous photosynthetic activity, suggesting the mangroves’ good health condition. The GCVI can complement the NDVI. However, the GCVI provides additional insight into the biochemical properties of vegetation. The GCVI is calculated as follows [75,76]:

$$\mathrm{GCVI}={\displaystyle \frac{\mathrm{NIR}}{\mathrm{Green}}}+1$$

(5)

where NIR and Green are the reflectance values of NIR and Green wavelengths of Landsat 5, 7, 8, and 9, respectively.

2.3.2. Elevation Masking

Generally, mangroves are found in low-elevation coastal areas. Therefore, we used the elevation mask on the band composite using the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) [77]. This analysis used elevation values of less than 60 m (<60 m). The elevation masking is critical because it removes highland areas from the band composite [78]. Hence, we modified the mangrove detection criteria by applying an elevation mask, focusing on low-lying areas, such as coastal areas, lagoons, and estuaries.

2.3.3. Threshold-Based Masking

Our study utilized threshold-based masks in the created band composite to further isolate potential mangrove areas. We used NDVI values (NDVI > 0.20) and MNDWI values (MNDWI > −0.50) based on previous studies to help eliminate background noise and non-mangrove and water areas [79,80]. Before applying the threshold values, we visually interpreted them in the context of mangrove detection in Sri Lanka. We verified the precision by applying several threshold values and adjusting them (for the NDVI from 0.0 and the MNDWI from −1.0) at each stage. The identified and used values were ideal for creating a temporal band composite to detect mangroves in Sri Lanka (see Figure 5).

2.3.4. Classification

Reference Samples

The accurate classification of mangrove versus non-mangrove areas depends on representative reference samples. In this study, a ‘sample’ refers to a single georeferenced point that has been labeled as either mangrove or non-mangrove based on reference data. Using locally distributed samples, we created a sample dataset (for training and validation) to detect mangroves in Sri Lanka. We used 70% of the samples for testing and 30% for validation (each class includes 2000 mangrove and 2000 non-mangrove samples, totaling 4000 samples per stage). The reference samples were obtained from topographic maps and other published maps from the Survey Department of Sri Lanka [52], published studies [51], Google Earth Pro [81], and visual interpretation of high-resolution satellite images (Table S3). During the mapping process, we also leveraged our expertise and understanding of the visual characteristics of mangroves, as well as our knowledge of the Sri Lankan coastal belt, to compile a reference dataset. Each classified map was cross-checked using ancillary data and visual interpretation. Misclassified areas were removed from the initial classified maps by comparing them with related data and knowledge of the Sri Lankan coastal belt.

Machine Learning Classification Using the Random Forest Method

The Random Forest (RF) classifier is selected because of its robustness and ability to handle multi-dimensional datasets [81,82]. The RF classification operates by combining multiple decision trees for classification tasks. RF can handle large datasets with multiple input variables, such as spectral bands and remote sensing indices, without overfitting. Moreover, RF can manage complex, non-linear relationships in land use and land cover, making it highly suitable for diverse and heterogeneous landscapes [81,82,83]. In our study, the number of trees and the number of random samples per node were 500 and 5, respectively. In this study, the RF classifier utilizes variables that include computed indices such as NDVI, NDMI, MNDWI, SR, and GCVI, as well as additional masks, including elevation, NDVI, and MNDWI (see Section 2.3.2 and Section 2.3.3). Figure 5 shows the selected locations of the band composites used in this study, along with the results after mangrove detection in 2022.

2.4. Mangrove Extent and Its Changes

This study estimated the extent of mangroves and their changes over time. The results were summarized at several spatial scales, including national, provincial, and district levels, as well as climatic zone levels, to support sustainable management practices and enable robust monitoring of mangroves in Sri Lanka (Figure 2 and Figure S1). Moreover, we investigated ten ecologically significant mangrove areas in Sri Lanka to identify changes in these areas over the study period (1987–2022). In the context of mangrove occurrence in Sri Lanka, we averaged the generated mangrove layers and categorized them into occurrence levels ranging from 100% to 10%. The results were discussed in relation to the conservation and long-term sustainability of mangroves in Sri Lanka.

2.5. Mangrove Structure and Stability Pattern Analysis

Analysis of mangrove structure and stability patterns is crucial for understanding landscape fragmentation, structural patterns, spatial configuration, and ecological functionality in Sri Lanka. Our study used six parameters, such as the Largest Patch Index (LPI), Perimeter-Area Ratio (PARA), Fractal Dimension Index (FRAC), Landscape Shape Index (LSI), Effective Mesh Size (MESH), and Number of Patches (NP) (Table S4), to provide a scientific basis for assessing mangrove fragmentation, connectivity, and habitat quality changes from 1989 to 2022. These indices are widely used in the ecological field and are particularly important for evaluating the health, structure, and functions of mangrove ecosystems [84,85]. In addition, the parameters collectively assist in exploring the impact of anthropogenic activities (urbanization, aquaculture, and deforestation) and natural pressures (sea-level rise and coastal erosion) [84,86].

We conducted a district-wise assessment to support government efforts and facilitate administrative monitoring of mangrove-related projects. Furthermore, district-level evaluations enable close tracking of mangrove sites, facilitate the initiation of restoration projects, and promote easier community engagement. This local approach also helps promote community-based mangrove management by using the knowledge of local people who are familiar with the mangrove ecosystems in their own districts. Including this local knowledge in conservation planning will strengthen mangrove protection efforts in Sri Lanka.

3. Results

3.1. Mangrove Changes in Sri Lanka

The mangroves in Sri Lanka exhibited significant changes from 1987 to 2022 due to anthropogenic activities, natural disturbances, and ecological regeneration processes (

Table 1. Total area of mangroves in Sri Lanka: National scale.

Year	1989	1995	1998	2004	2007	2010	2013	2016	2019	2022
Area (ha)	14,627	10,749	8596	8279	9820	10,640	11,837	13,755	15,490	16,615

). The average overall accuracy of the maps was over 96.19% (Table S5). The results show a sharp decline in mangrove extent from 14,627 ha in 1989 to 8596 ha in 1998 (Figure 6), which can be attributed to the civil war period, urban development, and the expansion of aquaculture. The Indian Ocean tsunami in 2004 further intensified mangrove loss (8279 ha by 2004), showing the lowest mangrove year during the study period. The massive tsunami waves uprooted mangrove trees, altered coastal geomorphology, and increased soil salinity, thereby affecting the mangrove regeneration process. However, some post-tsunami conservation practices and natural propagation helped to increase mangroves in the subsequent years (from 9820 ha in 2007 to 16,615 ha in 2022, increasing 6795 ha).

3.2. Provincial-Wise Mangrove Distribution in Sri Lanka

Provincial-wise mangrove distribution in Sri Lanka revealed significant spatial and temporal variations from 1989 to 2022 (

Table 2. Total area of mangroves in Sri Lanka (ha): Provincial-wise.

Year	Eastern	Northwestern	Northern	Southern	Western	Total
1989	6247.31	2268.2	2941.21	1737.33	1433.42	14,627.47
1995	5044.52	1685.03	1834.03	1204.65	980.73	10,748.96
1998	3622.01	1198.25	1338.68	1664.35	773.12	8596.41
2004	3244.97	1175.81	2016.32	1132.56	709.71	8279.37
2007	3861.75	1175.7	2325.56	1637.01	820.44	9820.46
2010	4585.96	1401.46	2120.09	1629.94	902.47	10,639.92
2013	4437.17	1679.46	3421.07	1561.15	738.01	11,836.86
2016	5347.86	1925.55	3933.97	1744.39	803.21	13,754.98
2019	5790.4	2079.73	4376.06	2381.79	861.95	15,489.93
2022	6293.6	2144.57	4556.73	2745.9	874.43	16,615.23
Average	4847.56	1673.38	2886.37	1743.91	889.75
Change	46.29	−123.63	1615.52	1008.57	−558.99

). The Eastern Province showed the highest extent of mangroves (Table 2). Despite their decline from 1989 to 2004, the Eastern Province gradually recovered from 2007, reaching 6294 ha in 2022 (Table 2). The Northern Province has experienced a considerable increase in mangrove coverage since 2004, with a significant net gain of 1616 ha by 2022, compared to the other provinces (Table 2). The Northwestern Province exhibited a continuous decline in mangrove extent until 2007, followed by a modest recovery from 2007 (Table 2). However, the net mangrove change remained negative from 1989 to 2022. The Southern Province indicates a fluctuating trend in mangroves (initial declines until 2004; a recovery stage showed an overall gain of 1009 ha by 2022) (Table 2), indicating enhanced mangrove conservation efforts and natural propagation processes. The Western Province showed a continuous decline (net loss of 559 ha from 1987 to 2022) due to the high urban expansion and intensive coastal infrastructure development. The persistent loss in the Western Province further indicates the significant pressure of human activities on mangroves.

3.3. District-Wise Mangrove Distribution in Sri Lanka

District-wise mangroves in Sri Lanka reveal substantial spatio-temporal changes from 1989 to 2022 (Table S6). Trincomalee, Puttalam, Ampara, Mannar, and Batticaloa districts showed the top five districts with the highest mangrove extent, respectively (Table S7). The districts with the lowest mangrove coverage were Kalutara, Colombo, and Matara (Table S7). Hambantota, Jaffna, and Trincomalee were the top three districts with the highest average mangrove gain (Table S8). At the same time, Gampaha, Ampara, and Batticaloa were the districts with the highest average loss from 1989 to 2022 in Sri Lanka (Table S9). The districts with historically larger mangrove extents, such as the Gampaha and Galle, showed significant declines over the study period. The Gampaha district had the most considerable mangrove extent (1252 ha in 1989), showing a sharp decline by 2004 and a partial recovery (820 ha in 2022). The Galle district showed considerable interannual variability (initial reduction in the 1990s and partial regeneration after 2010) (Table S6). In contrast, smaller mangrove extent districts, such as Colombo and Kalutara, showed a consistent mangrove loss. For example, the Colombo district dramatically decreased from 109 ha in 1989 to 29 ha in 2022 (an overall decline of 73%) (Table S6). Kalutara district showed a similar trajectory, with mangroves decreasing from 73 ha in 1987 to 26 ha in 2022 (an overall decline of 64%) (Table S6).

3.4. Mangrove Occurrence in Sri Lanka

Figure 6 shows the occurrence of mangroves in Sri Lanka from 1989 to 2022.

Figure 6. The occurrence of mangroves in Sri Lanka from 1989 to 2022. The labels in the figure indicate the most significant mangrove-bearing areas in Sri Lanka.

Figure 6. The occurrence of mangroves in Sri Lanka from 1989 to 2022. The labels in the figure indicate the most significant mangrove-bearing areas in Sri Lanka.

3.4.1. Mangrove Occurrence in Climatic Zone-Wise

Mangroves in Sri Lanka show distinct variations by climatic zone (Figure S1 and Table S10). The dry zone has the highest mangrove extent (11,383 ha in 100% occurrence), significantly surpassing the wet zone (2285 ha) and intermediate zone (760 ha), respectively (Table S10). Even at lower occurrence levels (60–90%), the dry zone consistently bears the largest extent of mangroves along the northeastern, eastern, southeastern, and northwestern coasts (the areas that belong to the dry zone in Sri Lanka)—particularly, Puttalam, Trincomalee, Vedithaththe, and Batticaloa. The Jaffna Peninsula also plays a vital role in the mangroves of the dry zone. The wet zone has a limited extent of mangrove (Table S10), particularly along the Kelani River estuary, Muthurajawela, and Negombo Lagoon. The intermediate zone has the least mangrove extent across all occurrence levels (Table S10).

3.4.2. Mangrove Occurrence in Eight Selected Areas

The eight locations were selected due to their high ecological significance, extensive mangrove coverage, unique geographical background, and contribution to coastal stability in Sri Lanka (Figure 7 and Table S11). Trincomalee, Puttalam, Batticaloa, Vidattativu, Chilaw Lagoon, Kalametiya, Vadamarachchi, and Muthurajawela Marsh are the most critical mangrove-bearing areas in Sri Lanka. The above locations vary in mangrove density due to coastal geomorphology, hydrodynamic conditions, and anthropogenic activities.

The results showed that Trincomalee Bay has the largest mangrove occurrence area (1724 ha at 100% occurrence) (Table S11). Batticaloa Lagoon has a considerable mangrove occurrence (3037 ha at 100% occurrence), Puttalam Lagoon (1365 ha at 100% occurrence), and Vedithalathive (989 ha, 100% occurrence) (Table S11). Furthermore, Vedithalative Nature Reserve is a mangrove protected area in Sri Lanka, declared under the Fauna and Flora Protection Ordinance (Gazette No. 1956/13) due to the significance of marine and coastal ecosystems under the Department of Wildlife Conservation. Chilaw Lagoon, Muthurajawela Marsh, and Kalamatiya have a lower mangrove occurrence (Table S11). Vadamarachchi, located on the Jaffna Peninsula, has relatively higher mangrove coverage. Therefore, policy plans and governance bodies should consider integrated coastal management strategies and site-specific assessments to protect these locations and control future mangrove degradation.

3.5. Spatio-Temporal Variations in Mangrove Structure and Stability Across the Districts

Mangroves in Sri Lanka are distributed across fourteen districts (Figure 1 and Table S1). The structural characteristics and stability patterns of mangroves in each district, with a focus on significant changes, are described below.

3.5.1. Ampara District

Primary fragmentation (decrease in LPI and MESH and an increase in NP) (Figure 8a) shows deforestation from 1997 to 2004. However, from 2016 to 2022, rapid fragmentation was evident (NP increased, while LPI and MESH decreased).

3.5.2. Batticaloa District

Severe fragmentation shows from 2016 to 2022 (decreasing in LPI and MESH, increasing in NP and PARA) (Figure 8b). The results further indicate that Batticaloa’s mangroves are more severely degraded, with significant fragmentation, loss of core patches, and increased isolation.

3.5.3. Colombo District

The Colombo district exhibits a decline in LPI and MESH, accompanied by increases in NP, indicating signals of fragmentation and shrinking core mangrove areas from 2007 to 2022 (Figure 8c). Recent years have shown a decline in the spatial integrity of Colombo’s mangrove, particularly in terms of connectivity (MESH) and dominance (LPI) (Figure 8c).

3.5.4. Galle District

Moderate stabilization was observed from 2007 to 2022 (Figure 8d).

3.5.5. Gampaha District

Mangroves in the Gampaha districts exhibit high spatial coherence and ecological resilience, as indicated by fewer patches, a strong LPI, compact shapes, and high MESH values from 2010 to 2022 (Figure 8e). Despite past fragmentation (the 1990s), Gampaha exhibits strong recovery, with high dominance, low edge stress, and increased connectivity.

3.5.6. Hambantota District

The increase in connectivity (MESH) and fragmentation (increasing NP) suggests clustered small patches coexisting with a strong core patch from 2013 to 2022 (Figure 8f). Overall, the mangroves in the Hambantota district indicate moderately fragmented but resilient ecosystems, exhibiting both signs of degradation and spatial adaptation by 2022.

3.5.7. Jaffna District

From 2004 to 2016, LPI, MESH, and LSI increased, indicating a clear restoration stage (Figure 8g). However, increased NP, combined with high MESH retention, suggests clustered patch growth rather than separated fragmentation from 2019 to 2022 (Figure 8g). However, the increase in NP is an early indicator of potential fragmentation (Figure 8g), necessitating continuous monitoring and protective strategies.

3.5.8. Kalutara District

Kalutara district mangrove trends show that LSI, FRAC, and MESH decreased (Figure 8h), indicating a simplification of shape, a decrease in connectivity, and an increased LPI from 2003 to 2016 (Figure 8h). Although LPI shows a high value, the mangroves are ecologically fragile, with low MESH and high PARA (Figure 8h), indicating urgent conservation and restoration strategies.

3.5.9. Kilinochchi District

The mangrove trends in the Kilinochchi district show reduced connectivity and remain as small, disconnected, and structurally weak patches due to degradation from 1989 to 2016 (Figure 8i). Spatial cohesion also improved (MESH increase) (Figure 8i), showing a modest but favorable ecological recovery, especially after 2019 (Figure 8i).

3.5.10. Manner District

The mangrove district in Manner shows increased fragmentation but stable core areas (LPI) and strong connectivity (MESH) from 2010 to 2022 (Figure 8j). Overall, the Mannar district has a resilient mangrove ecosystem, despite fragmentation, with functional connectivity and structural stability, indicating a stable and well-connected mangrove system.

3.5.11. Matara District

Mangroves in the Matara district exhibit an increased LPI, LSI, and MESH, indicating a recovery period from 2010 to 2016 (Figure 8k). However, from 2019 to 2022, the number of new mangrove patches increased while LPI and MESH decreased, suggesting a new fragmentation trend in the Matara district.

3.5.12. Mullaitivu District

The Mullaitivu district mangrove shows the best ecological structure from 2010 to 2016 (high LPI and MESH, low PARA) (Figure 8l). However, mangrove fragmentation increased from 2019 to 2022, resulting in a decrease in LPI.

3.5.13. Puttalam District

Puttalam district mangroves show stable but gradually decreasing connectivity and patch dominance from 1997 to 2016 (Figure 8m). Notably, extensive fragmentation (NP) and declining LPI, MESH, and LSI show clear signs of ecosystem degradation from 2019 to 2022 (Figure 8m), indicating that the Puttalam district is one of the most fragmented mangrove systems in Sri Lanka.

3.5.14. Trincomalee District

Mangroves in the Trincomalee district show LPI > 35% (stable core areas), steadily increasing MESH (connectivity), and relatively higher fragmentation (Figure 8n), showing Trincomalee’s mangroves are a healthy but vulnerable ecosystem from 2010 to 2022 (Figure 8n), indicating strong ecological performance, with a dominant core patch.

3.6. Comparisons with Global Mangrove Watch (GMW) Data

Our study demonstrates higher accuracy (average overall accuracy = 96.29%) in mangrove mapping compared to the GMW dataset (87.4%) [39], primarily due to the use of national-level training and validation data. Conversely, the GMW dataset relies on global-scale training data. Using national-scale data enhances mapping accuracy by incorporating region-specific spectral and ecological characteristics. Our mangrove detection methodology primarily focuses on coastal mangrove mapping, which may lead to a more precise delineation of mangroves (see Section 2.2). In contrast, the GMW dataset employs a more generalized global mangrove detection typology, which may lead to overestimation or underestimation in certain areas due to the diverse ecological environments of mangroves worldwide. The GMW dataset consistently shows higher mangrove area values compared to our study due to differences in classification methodologies and the resolution of input data (Figure 9).

4. Discussion

4.1. Ecological Significance of Mangroves in Sri Lanka

Mangroves in Sri Lanka are one of the critical ecosystems, providing valuable ecosystem services such as carbon storage, biodiversity, habitat quality, nutrient cycling, and the stability of coastal areas. Our study revealed that mangroves are distributed along lagoons, estuaries, bays, and riverbanks near the coastal zone, covering approximately 16,615 ha by 2022, representing 0.25% of Sri Lanka’s land area. The most extensive and continuous mangrove patches are observed in Puttalam, Kalpitiya, the Jaffna Peninsula, Trincomalee, Batticaloa, Muthurajawela, and Vediththalative coastal regions (Figure 7 and Figure 8). However, ecologically and biologically diverse small mangrove patches are found in Negombo Lagoon, Madu Ganga, Rekawa Lagoon, Kalamatiya, Koggala, and Anivilundawa. The Colombo, Kalutara, Galle, Matara, Ampara, and Puttalam districts exhibit a fragmented mangrove distribution, largely due to high levels of anthropogenic activities, including urban and coastal development. These areas undoubtedly provide valuable ecosystem services and carbon sequestration benefits, thereby contributing to Sri Lanka’s environmental sustainability and global efforts to mitigate climate change.

The geographical context of Sri Lanka influences the distribution pattern of mangroves. For example, the eastern coastal belt of Sri Lanka is primarily affected by cyclonic activities from the Bay of Bengal, seasonal flooding and sediment deposition, and limited human activities, which provide suitable ecological conditions for propagating pristine mangrove patches. Conversely, the western coast faces the Bay of Arabia and has a more stable hydrological condition; however, significant urban expansion and coastal development pressures impact the sustainability of these areas [57]. The Intertropical Convergence Zone (ITCZ) modulates rainfall and river discharge patterns in Sri Lanka, affecting nutrient dynamics in estuaries and the lagoon system [87,88]. In Sri Lanka, 103 rivers from the central and southern highlands transport numerous sediments and organic matter to estuaries and lagoon systems (particularly in the dry zone), which is crucial for the mangrove ecosystem in Sri Lanka. Sri Lanka is home to numerous true mangroves, including critically endangered species such as Lumnitzera littorea, Ceriops decandra, and Xylocarpus rumphii, emphasizing the conservation importance of these mangroves [51]. Our study provides the initial assessment of mangrove occurrence in Sri Lanka, which will undoubtedly provide primary information to identify distribution patterns and changes, as well as monitor endangered and other mangrove species.

Our findings contribute to the international context by providing the first national-scale mangrove extent in Sri Lanka, using remote sensing techniques, which enables cross-country comparisons with other countries in the tropical regions. The results of fragmented versus intact mangrove patches provide new empirical findings to global discussions on the balance between anthropogenic activities and natural climatic drivers related to mangrove resilience. Our study further provides valuable insights for regional integration into blue carbon estimation and international conservation strategies.

4.2. Multi-Dimensional Challenges to Mangrove Sustainability in Sri Lanka

Mangroves in Sri Lanka face numerous threats that jeopardize their long-term sustainability due to anthropogenic activities, land-use change, coastal development, deforestation, and illegal exploitation, as well as climate change, pollution, water quality degradation, biodiversity loss, weak policy implementation, and governance issues [51]. Among them, anthropogenic activities have historically been and continue to be a primary influencing factor of mangrove loss and degradation in Sri Lanka [57]. Deforestation and changes to mangrove habitats for land use have significantly impacted ecological interactions and ecosystem functions [89]. Studies have reported that many mangroves have been cleared for aquaculture development [90]. In Sri Lanka, aquaculture is particularly prominent in the Northwestern, Southern, and Eastern Provinces—especially around areas such as Batticaloa in the Eastern Province and Chilaw Lagoon in the Northwestern Province—significantly affecting mangrove cover [51].

Mangrove transformation for agriculture (paddy cultivation and coconut plantations), mangrove land reclamation for many large projects, and illegal encroachments [51], as well as urbanization and infrastructure development (the construction of hotels, settlements, roads, and ports (air and harbor)) [59], have also contributed significantly to mangrove loss in many districts in Sri Lanka (Table S6). The results of the national-scale assessment indicate a continuous increase in mangrove cover, particularly from 2007 to 2022 (Table 1). Many districts where human activities are predominant, such as Colombo, Gampaha, Kalutara, Matara, and Galle, exhibit a rapid loss and degradation of mangroves (Table S6). Undisturbed and low-activity districts, such as Hambantota, Jaffna, Trincomalee, Mannar, Kilinochchi, and Mullaitivu, exhibit an increasing trend (Table S6). Therefore, the national-scale mangrove assessment shows an increasing trend (Table 1). In contrast, district-wise assessments show significant spatio-temporal variations in gaining and losing (Table S6), as well as greater variations in structure and stability patterns (Figure 8). Athukorala et al. (2021) revealed that Muthurajawela Marsh and Negombo Lagoon, located in the Colombo Metropolitan Region, showed dramatic degradation of mangroves due to urban development [59]. Socioeconomic factors and unsustainable resource exploitation pose significant challenges to the conservation of mangroves in Sri Lanka. For example, local communities living near lagoons and estuaries (where extensive mangrove areas exist) depend on resources such as fuelwood, timber, and fisheries, indicating sustainable management of overexploitation and degradation of mangrove habitats [51]. Sea level rise (a direct impact of global warming) creates a considerable risk to mangrove habitats via permanent inundation and fluctuation in salinity levels [91].

The spatio-temporal changes in mangroves in Sri Lanka exhibit general global trends, where localized mangrove loss persists alongside regional gains resulting from restoration and natural regeneration processes. Similar patterns have been observed in Southeast Asia, West Africa, and Latin America [39]. Our results further emphasize the urgent need for integrated, multi-scale monitoring and management strategies. Mangrove assessment in Sri Lanka provides robust evidence to inform global initiatives, such as the Paris Agreement and the UN Decade on Ecosystem Restoration, as well as blue carbon strategies that focus on blue carbon estimation, identifying priority restoration sites, and mangrove conservation for a cost-effective climate mitigation strategy.

4.3. Integrating Spatial Structure and Conservation Strategies

Numerous studies such as the Mekong Delta in Vietnam [92], the tallest mangrove forest on Earth (Pongara National Park, Gabon) [46], the largest continuous mangrove forest in the world (Sundarbans, Bangladesh) [93], mangroves in the southwestern coast of China [94], Pongok Island [95], the Pearl River Estuary, China [96], the coastal regions of Hong Kong, China [97], the Arabian Peninsula [90], Bonaire (a small island in the Caribbean Sea) [49], and mangroves in Indonesia [98], have demonstrated that integrating mangrove change detection and the spatial distribution and stability patterns significantly enhances mangrove conservation efforts globally and supports identifying site-specific restoration priorities. The following recommendations suggest strategies for sustaining Sri Lanka’s mangroves. We discuss district-wide strategies for enhancing mangrove ecosystems and their combined ecosystem services (see Figure 8 and Table S6).

–

Ampara and Batticaloa districts require urgent conservation and policy actions, including reforestation, zoning restrictions, and community-based conservation.

–

Colombo, Gampaha, and Kalutara districts should integrate ecological corridors and buffer zones into urban planning, incorporate mangrove protection into climate-resilient city planning, and prioritize the restoration of fragmented patches.

–

Galle, Matara, Hambantota, and Mullaitivu districts require restoring connectivity between patches, with a focus on preserving the remaining core areas and monitoring coastal development. Buffer zone enforcement and the protection of emerging small patches are also required.

–

Jaffna exhibits remarkable mangrove recovery; however, the rise in NP is an early signal of potential fragmentation, necessitating continuous monitoring and mangrove landscape conservation.

–

Puttalam, Trincomalee, Kilinochchi, and Mannar districts require urgent habitat consolidation, the development of restoration corridors, protection from aquaculture expansion, and the implementation of ecosystem-based management and blue carbon conservation strategies, the establishment of habitat corridors, and the protection of emerging clusters.

Furthermore, ensuring the long-term sustainability of mangroves in Sri Lanka requires comprehensive strategies that combine policy enforcement, scientifically based ecological restoration, community engagement, and pollution control. In this regard, the National Guidelines for the Restoration of Mangrove Ecosystems in Central Environmental Authority [99], the Fauna and Flora Protection Ordinance of 1937 under the Wildlife Department [100], the World Conservation Union (IUCN), Sri Lanka [101], the National Aquatic Resources Agency (NARA) [102], the National Science Foundation (NSF) [103], and the Coast Conservation Department (CCD) [64] all play a unique role in mangrove protection and conservation in Sri Lanka. They should closely relate to all departments that are important for mangroves in Sri Lanka. Moreover, the Urban Development Authority [104] and the Road Development Authority [105] should closely monitor recent rapid urbanization and infrastructure development in coastal cities where mangrove forests are located.

In recent years, Sri Lanka has demonstrated a strong national obligation to mangrove conservation and ecosystem restoration. In 2015, it became the first country to legally protect all mangroves, establishing the National Expert Committee on Mangrove Conservation and Sustainable Use [106]. This was followed by the adoption of the National Policy on Conservation and Sustainable Utilization of Mangrove Ecosystems in 2020 and the development of a National Strategic Action Plan and accompanying guidelines in 2022 aimed at ensuring long-term sustainability. Recognizing these sustained efforts, Sri Lanka’s mangrove restoration initiative was designated as a World Restoration Flagship and recognized as one of the most outstanding examples of large-scale and long-term mangrove ecosystem restoration globally [107]. Integrating the national climate change mitigation plan into mangrove conservation is crucial for mitigating threats to the mangrove ecosystem.

4.4. Uncertainties and Future Directions

Landsat data is useful for long-term and large-scale mapping (30 m resolution). However, Landsat data may not be sufficient to identify small or fragmented mangroves or to distinguish between different mangrove species. Future studies should utilize higher-resolution imagery (e.g., Sentinel-2 (10 m), WorldView, PlanetScope) for future mangrove mapping. Landsat data support long-term temporal analysis of mangrove distribution, while higher-resolution satellite data offer more detailed spatial data in the study areas. Furthermore, using high-resolution images will improve the accuracy of mangrove detection and may enable the separation of mangrove species. Future studies should utilize extensive field data, including species composition, biomass, soil characteristics, and other relevant parameters, to inform mangrove change detection.

5. Conclusions

This study presents the first national-scale assessment of mangrove dynamics using remote sensing in Sri Lanka. We developed a mangrove detection approach using Landsat images in the GEE to analyze mangrove changes in Sri Lanka from 1987 to 2022. Mangrove gain, loss, occurrence, structure, and stability patterns were analyzed at the national scale, provincial, district-wise, and climatic zone-wise. The results showed that mangroves in Sri Lanka have experienced significant spatio-temporal changes. The results indicated that mangrove extent increased from 1989 to 2022 (13.6%), suggesting a continuous recovery of mangroves at the national scale. Mangrove structure and stability pattern analysis revealed that the districts that perform best ecologically are Jaffna, Trincomalee, and Gampaha. Moderate to transitional districts include Ampara, Mannar, Matara, Galle, Mullaitivu, Hambantota, and Kilinochchi. The most degraded districts are Batticaloa, Puttalam, Colombo, and Kalutara.

The dry zone of the country bears the largest extent of mangrove (11,383 ha). In contrast, the wet (2285 ha) and intermediate zones (760 ha) have lower coverage. Therefore, integrating mangrove management into national climate strategies, sustainable land use, science-based restoration, and community engagement will undoubtedly support the balance between the ecological and socioeconomic benefits of mangroves in Sri Lanka.