Assessment of Critical Environmental Risk Zones Around Wetlands in Colombo,Sri Lanka,Using Satellite Remote Sensing and Multi-criteria Decision Tools

Abstract

Colombo, Sri Lanka’s commercial capital, is recognized as one of the first 18 global sites that were awarded the Ramsar Wetland City accreditation, and is a prime example of the intersection between urbanization and wetland conservation. Thus, this study aims to conduct an environmental risk assessment that can provide insights into the changes in biophysical properties driven by urbanization over the 2000–2020 period. The study used Geographical Information Systems and a remote sensing approach to assess critical environmental risk zones around urban freshwater lakes in the Colombo district of Sri Lanka, including Thalangama, an environmental protection area experiencing sub-urbanization. Satellite data (2000–2020) were employed, and spectral indices, including NDVI, NDBI, NDWI, NDMI, RVI and LST, were calculated. Intersecting areas with multiple risk factors were determined, and distances from these zones to lakes were calculated to generate spatially explicit risk maps using inverse distance weighting. The building density has consistently increased from 2000 to 2020 in all three sites, with ‘very high’ density areas expanding notably. NDVI reveals a gradual increase in ‘very high’ categories until 2015. Plant-water stress analysis shows significant water stress in 2015, and water body areas have decreased after 2010 in all the sites. Multi-criteria assessment identifies critical environmental risk zones around urban lakes, indicating high vulnerability to environmental damage and degradation. Overall, the study emphasizes the importance of leveraging data and risk analysis to inform decision-making processes in environmental management. By doing so, policymakers can enhance the effectiveness of conservation measures and promote the long-term sustainability of natural resources.

Keywords

Environmental risk zones wetland management urbanization land-use changes

Introduction

Accelerated urbanization has become a major concern that impacts living beings on a regional and global scale. The dramatic expansion of urbanized areas under population pressure and altered land use and land cover (LULC) have become central to a series of urban-oriented environmental challenges (Giller et al., 2004; Maitima et al., 2009). This situation is significant in tropical countries where rivers, lakes and wetlands are some of the most degraded ecosystems which, unfortunately, play a major role in urban environment conservation and restoration (Millennium Ecosystem Assessment, 2005). Sri Lanka is a tropical island nation that possesses a great variety of wetland ecosystems (Kotagama & Bambaradeniya, 2006). Colombo, Sri Lanka’s commercial capital was recognized as one of the first 18 global sites that were awarded the Ramsar Wetland City accreditation (Amarasinghe & Ryan, 2018): the only such commercial capital to be bestowed with this honour in the world (Mombauer, 2019). However, accelerated urbanization within the Colombo district (a district is one of 25 administrative units in Sri Lanka; Figure 1), an area with a complex network of wetlands, has seen more than 40% of its wetlands disappearing during the last decade due to the direct and indirect impacts of urbanization (GoSL, 2016). The current rate of wetland loss in the Colombo Metropolitan region alone is approximately 1.2% per annum (GoSL, 2016). If this trend continues, the area of wetlands will be reduced by one-third by 2038 and by one-half by 2070 (GoSL, 2016).

Figure 1.

Source: Gunathilaka (2020).

Urbanization and its impacts in the Colombo district have been well documented. Gunathilaka (2020) showed that over the years, most of the paddy and marshlands in Colombo have been abandoned or converted to built-up areas while Ranagalage et al. (2018) performed spatial clustering to demonstrate hot spots and cold spots of building and vegetation densities in the Colombo district. They showed that the pattern of hot spots, particularly associated with the urban heat island effect, has shifted gradually from the western to the eastern regions within the Colombo district over the past 20 years. Out of 182 administrative divisions, about 32.7% have consistently remained as hot spots. Interestingly, 43 divisions initially not considered significant have now become hot spots, indicating their vulnerability to future changes. Additionally, 49 divisions are predicted to emerge as hot spots. Notably, these hot spots predominantly appear in the western part of the district, while the colder spots are concentrated in the eastern areas. This changing spatial distribution of emerging urban heat island hot spots closely mirrors the urban development pattern seen in the Colombo district. Prominent suburbs of Colombo (e.g., Sri Jayawardhanapura, Kaduwela, Battaramulla, Kesbewa, Maharagama, Homagama and Athurugiriya) also have been documented for rapid urbanization (Ranaweera & Ratnayake, 2017). Urbanization has significant implications for wetlands, leading to a multitude of challenges that can greatly impact these delicate ecosystems and the surrounding communities.

The conversion of natural landscapes to urban areas brings about numerous adverse effects on wetlands, including habitat destruction, pollution and altered hydrology. One of the primary concerns with urbanization is the loss and fragmentation of wetland habitats (Gunathilaka et al., 2022). As cities expand, wetlands are often drained, filled in, or otherwise altered to accommodate infrastructural development and human settlements. This destruction and fragmentation can lead to the loss of critical habitats for a wide variety of plant and animal species, resulting in decreased biodiversity and ecosystem resilience. Urbanization also contributes to increased pollution of wetlands through the discharge of stormwater run-off and industrial pollutants. Urban areas produce large volumes of surface water run-off, which can carry contaminants such as heavy metals, fertilizers, pesticides and other harmful substances into nearby wetlands. These pollutants can degrade water quality, disrupt ecological processes and harm resident flora and fauna, posing risks to the overall health and functioning of wetland ecosystems. Moreover, altered hydrology due to urbanization can have profound impacts on wetlands. The construction of impervious surfaces, such as roads, buildings and parking lots, can disrupt natural water flow patterns, leading to changes in groundwater recharge, surface water run-off and sediment transport (Hettiarachchi, 2018; Hettiarachchi et al., 2014). These alterations can result in reduced water availability, increased flooding and changes in sediment deposition within wetlands, ultimately affecting their ecological balance and functions (Gunathilaka & Harshana, 2022).

The ramifications of urbanization on wetlands extend beyond the ecological realm and can also impact surrounding communities. Wetlands serve crucial functions such as flood mitigation, water purification and erosion control, all of which contribute to the well-being of local communities. Urbanization-induced degradation of wetlands can thus increase the vulnerability of nearby areas to flooding, water pollution and other environmental hazards, jeopardizing the safety and livelihoods of people living in these areas.

Urbanization creates environmental risks in several ways (Abdulaziz et al., 2023; Lu et al., 2022). First, it leads to the loss of natural habitats and ecosystems due to the construction of buildings, roads and infrastructure. This results in the displacement or extinction of many plant and animal species. Second, urbanization increases the demand for resources such as water and energy, which puts pressure on natural resources and contributes to pollution and over-consumption. Third, the concentration of population in urban areas leads to increased pollution levels, including air pollution from industries and vehicles and water pollution from inadequate waste management systems.

Assessing environmental risk is important because it helps us understand the potential harm or damage that can be caused by human activities or natural events (Zhou et al., 2020). It allows us to identify and prioritize areas or activities that pose the greatest risk to the environment and develop strategies and policies to mitigate or manage these risks effectively. Environmental risk can be evaluated using risk zones, which categorize areas based on the level of risk they face (Cheng et al., 2023). These zones can be defined based on factors such as pollution levels, vulnerability to natural disasters or the extent of habitat destruction. By categorizing areas into risk zones, decision-makers can prioritize resources and interventions based on the severity of the risk (Guo et al., 2022). For example, areas in high-risk zones may require more stringent regulations or targeted conservation efforts, which in the long run provide a framework for sustainable development. By considering environmental risks and incorporating risk-zone mapping into urban planning processes, decision-makers can make informed choices about where and how to develop urban areas. This helps to minimize the negative impacts on the environment and maximize the benefits of urbanization. Mapping can guide decisions on locating industrial zones away from vulnerable ecosystems or areas prone to natural disasters. It can also inform decisions on the allocation of green spaces and the protection of critical habitats. Overall, assessing environmental risk using risk zones is crucial for sustainable urban development. It enables decision-makers to prioritize and allocate resources effectively, minimize environmental damage and make informed choices that promote long-term environmental and societal well-being.

Multi-temporal satellite remote sensing and Geographical Information Systems (GIS) offer efficient methods for analysing urbanization trends (Firozjaei et al., 2023). Satellite remote sensing allows for faster and efficient data collection, repetitive sampling and coverage of large geographic regions in contrast to terrestrial surveys and field work. Fousseni et al. (2011) used Landsat data to detect urban area vegetation changes in the capital city of Togo, while Fonseka et al. (2019) used Landsat data to identify the impact of urban expansion on surface temperature changes for 28 years in Colombo, Sri Lanka. The relationship between urbanization and road networks in the lower northeastern region of Thailand was compared to the urban area in 2006, 2013 and 2016 using nighttime light satellite images that showed urbanization has a significantly positive relationship with the road network at the 0.01 level, with R² values of 0.800 for urbanization and 0.985 for the road network (Kulpanich et al., 2022). Bose et al. (2023) focused on urban changes using satellite images to study how urbanization took place in Kolkata and Bhubaneswar—two most important cities in eastern India where urban growth has taken place rapidly over 30 years.

Therefore, it is of timely importance to identify environmental risk zones in Colombo and its suburbs as one of the first steps in conserving and potentially restoring urban wetland ecosystems. This study aims to carry out an environmental risk assessment by assessing the changes in selected biophysical properties driven by urbanization for the period 2000–2020 to evaluate environmental risk zones and levels of highly degraded urban freshwater ecosystems in the suburbs of Colombo, Sri Lanka. The significance of these biophysical parameters lies in their ability to indicate environmental health and changes. Vegetation indices (e.g., Normalised Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI)) reveal plant health and density, water indices (e.g., Normalised Difference Water Index (NDWI) and MNDWI) show water coverage and quality, moisture indices (e.g., Normalised Difference Moisture Index (NDMI)) indicate soil and plant water stress, and temperature measures (e.g., land surface temperature (LST)) highlight heat islands and surface temperature changes. Building density indices (e.g., Normalised Difference Building Index (NDBI)) reflects urbanization impacts. A multi-criteria analysis mechanism, specifically the weighted overlay method (WOM), integrates these diverse biophysical parameters to produce a comprehensive risk assessment. This method assigns different weights to each parameter based on its significance in contributing to environmental risk. The parameters are reclassified and combined to create a composite risk map, highlighting areas of high, moderate and low risk. This approach allows for a nuanced understanding of environmental risks, considering multiple factors and their interactions. It is hoped that the results of this study will be used to develop plans for multiple uses of natural resources and nature conservation and as a baseline for decision-making at different levels of land-use planning.

Methodology

Study Area

The study focuses on urban freshwater lakes located in the Colombo district of Sri Lanka (Figure 1). Three urban lakes are selected representing three Divisional Secretariat Divisions (DSD) within the Colombo district (A DSD is a sub-administrative unit of a district.): (a) Boralesgamuwa Lake located within the Maharagama DSD, (b) Kesbewa Lake within the Kesbewa DSD and (c) Thalangama Lake within the Kaduwela DSD. The Thalangama Lake is also an environmental protection area (EPA) where the sub-urbanization process is significant and highly dynamic.

Satellite Data Collection

This study follows Haque et al. (2020) conducted in Khulna City, Bangladesh, where the presence/absence and distribution of vegetation, building density, surface water extent and heat islands (through LST) are used as impacting factors in creating environmental risk zones. These are representative not only of LULC but also of meteorological conditions in the region. Satellite remote sensing is used to assess these environmental parameters.

Five Landsat satellite images (Table 1) between the years 2000 and 2020 at consecutive five-year gaps were used to study physical, socio-economical and meteorological changes in the study area. Specific parameters analysed (Table 2), followed by the spectral indices used, are as follows: vegetation changes (NDVI), building density (NDBI), water extent changes (NDWI), plant-water stress (NDMI), vegetation healthiness (RVI), temperature changes (LST) and heat island impacts. All the satellite images were downloaded from the United States Geological Survey and taken in January or February to avoid seasonal rainfall effects and had less than 10% of cloud cover. Analysis was performed at a spatial resolution of 30 metres (native resolution of spectral bands used in the Landsat imagery).

Table 1.

Details of Landsat Images.

Year	Filename	Date of Acquisition	Path	Row	Spatial Resolution	Satellite
2000	L71EDC1800023160100_HDF.170441756	23 January 2000	141	55	30 × 30	Landsat 7
2005	LT05_L1TP_141055_20050213_20161127	13 February 2005	141	55	30 × 30	Landsat 5
2010	LT51410552010026BKT00	26 January 2010	141	55	30 × 30	Landsat 5
2015	LC81410552015008LGN01	8 January 2015	141	55	30 × 30	Landsat 8
2020	LC81410552020054LGN00	3 February 2020	141	55	30 × 30	Landsat 8

Table 2.

Spectral Bands Used to Calculate Selected Indicators.

Indicators	Landsat 5		Landsat 7		Landsat 8
Indicators	Band Number	Band Name	Band Number	Band Name	Band Number	Band Name
NDVI	4, 3	NIR, RED	4, 3	NIR, RED	5, 4	NIR, RED
NDWI	2, 4	GREEN, NIR	5, 7	SWIR1, SWIR2	5, 6	NIR, SWIR1
NDBI	4, 5	NIR, SWIR	6, 5	THERMAL IR, SWIR1	6, 4	SWIR, RED
NDMI	4, 5	NIR, SWIR	4, 5	NIR, SWIR1	5, 6	NIR, SWIR1
LST	6	THERMAL IR	6	THERMAL IR, SWIR1	10, 5, 4	TIRS1–NIR–RED
RVI	4, 5	NIR, SWIR	4, 5	NIR, SWIR1	4, 5	RED, NIR

Normalized Difference Vegetation Index (NDVI): This is an index of plant greenness or photosynthetic activity proposed by Rouse Jr. et al. (Rouse, 1974). NDVI separates green vegetation from other surfaces as the chlorophyll of green vegetation absorbs red light for photosynthesis and reflects the near-infrared (hereinafter NIR) wavelengths due to scattering caused by internal leaf structure (Tucker, 1979) and therefore can act as a proxy for vegetation distribution. The values of NDVI range from –1.0 to +1.0 and values closer to +1 indicate high leaf biomass, canopy closure, or leaf area (Jasinski, 1990). The ease of calculating NDVI from satellite data, the success of detecting vegetation, and interpretation have made this one of the most widely used vegetation indices and popular spectral vegetation index (Govil et al., 2019; Purevdorj et al., 1998; Wilson & Sader, 2002).

Equation (1). Normalized Difference Vegetation Index:

NDVI = \frac{NIR - R}{NIR + R} .

Ratio Vegetation Index (RVI): Originally described by Birth and McVey (1968), RVI is a slope-based vegetation index similar to NDVI (Silleos et al., 2006) that is widely used for green biomass estimations and monitoring (Xue & Su, 2017).

Equation (2). Ratio Vegetation Index:

RVI = \frac{NIR}{R},

where NIR is the near-infrared band reflectance and R is the red band reflectance. Bushy plants have low reflectance on the red band and correlate with leaf area, leaf dry biomass and chlorophyll content of leaves (Quan et al., 2011). RVI values range between 0 and +1, in which values close to +1 express increasing vegetation cover and non-vegetated areas are close to 0 (Agapiou, 2020). The main difference between RVI and NDVI is that RVI has a great potential to assess insect pest damage on vegetation (i.e., plant health) (Tan et al., 2019).

Normalized Difference Moisture Index (NDMI): Proposed by Hardisky et al. in 1984 (Hardisky et al.,1984), NDMI contrasts the NIR band 4, which is sensitive to the reflectance of leaf chlorophyll content to the mid-infrared (MIR) band 5, which is sensitive to the absorbance of leaf moisture calculated using the following formula:

Equation (3). Normalized Difference Moisture Index.

NDMI = \frac{R - NIR}{R + NIR} .

NDMI is also a dimensionless quantity that ranges from –1 to +1. High values indicate high water content and sufficient moisture, and the low values indicate water stress on the land surface (Xu, 2006). The NDMI is the best index to identify the soil and moisture content present in a satellite image (Gowri & Manjula, 2019).

Since the NDMI analysis helps to predict the drought severity of a particular area most of the studies focus on plant moisture content to assess ecosystems. Water stress generally leads to increased plant respiration and coping with dry factors via stomatal closure which in turn reduces the leaf area of plants (Assal et al., 2016). During such periods, photosynthetic activity goes down, and changes can be observed in the canopy structure. The collective results of these phenomena are a decrease in chlorophyll and water content of plant leaves (Jones & Vaughan, 2010).

Normalized Difference Water Index (NDWI): Introduced by McFeeters (1996) to detect surface water in wetland environments and measure surface water dimension (Ali et al., 2019), NDWI is also used for visualizing the turbidity differences (Herbei et al., 2016).

Equation (4). Normalized Difference Water Index.

NDWI = \frac{Green - NIR}{Green + NIR}

NDWI uses the green and near-infrared bands. The NDWI values range from –1 to +1. Negative values indicate land, soil and live vegetation cover while positive values indicate water coverage.

Normalized Difference Building Index (NDBI): Used to extract built-up features (Govil et al., 2019; Zha et al., 2003), NDBI has values ranging from –1 to 1. It represents the density of the built-up area on the land surface from the ratio between the difference of the sum of the near-infrared and SWIR refracted radiation (Haque et al., 2020) of satellite imagery using equation (5). High values indicate a higher density of built-up land/urban/developed areas, while low values indicate less built-up and/or rural/undeveloped areas (Kafy et al., 2020).

Equation (5). Normalized Difference Building Index.

NDBI = \frac{SWIR - NIR}{SWIR + NIR} .

Land Surface Temperature (LST): This is the surface temperature of the earth. Identification of urban heat island impacts is typically based on LST (Anandababu et al., 2018). In this study, thermal infrared digital numbers were first converted to top of the atmosphere spectral radiance.

Equation (6). Top of Atmosphere Radiance.

L λ = M L * Q c a l + A L,

where Lλ is the top of atmosphere radiance (Watt/m² * sr * µm), ML is the radiance multiplicative band (no.), AL is radiance adds band (no.)

Qcal show the quantized and calibrated standard product pixel values (DN).

Spectral radiance data were converted to top-of-atmosphere brightness temperature using thermal constant values in the metadata file.

Equation (7). Top of Atmosphere Brightness Temperature:

B T = \frac{K 2}{\ln (\frac{k 1}{L λ} + 1) - 272.15},

where

BT is the top-of-atmosphere brightness temperature (^oC), Lλ is the top-of-atmosphere spectral radiance (Watt/m² * sr * µm), * K1 is the K1 constant band, * K2 is the K2 constant band (* numeric coefficients specific to the bands or spectral regions being observed).

To calculate land surface emissivity, NDVI should be calculated (Anandababu et al., 2018). Land surface emissivity is the average emissivity of an element on the surface of the earth calculated from NDVI values (Anandababu et al., 2018).

Equation (8). Land Surface Emissivity (1):

P V = {(\frac{NDVI - {NDVI}_{min}}{{NDVI}_{max} + {NDVI}_{min}})}^{2},

where PV is the proportion of vegetation, NDVI is the digital number value from NDVI, NDVI_min is minimum digital number value from NDVI and NDVI_max is the maximum digital number value from NDVI.

Equation (9). Land Surface Emissivity (2).

E = 0.004 * P V + 0.986,

where E is the land surface emissivity and PV is the proportion of vegetation.

LST is the radiative temperature calculated using the top-of-atmosphere temperature, the wavelength of emitted radiance.

Equation (10). Land Surface Temperature.

LST = (\frac{B T}{1}) + W * (\frac{B T}{144380}) * \ln (E),

where BT is the top-of-atmosphere brightness temperature (^oC), W is the wavelength of emitted radiance and E is the land surface emissivity.

Data Analysis

The different data layers (raster) that were calculated (i.e., NDVI, RVI and NDBI) were subject to a multi-criteria assessment using the WOM based on Haque et al. (2020). In this study, WOM was chosen to identify environmentally risky areas. This method helps model risk zones, with higher values indicating more risk and lower values indicating less risk. For each of the rasters, based on their minimum and maximum values, they were reclassified into five risk levels: very high, high, moderate, low and very low. This categorization likely helps in simplifying and visualizing the data, allowing for easier interpretation of risk levels associated with different variables such as vegetation density, building density, surface temperature, moisture levels and elevation. From each of the rasters low vegetation, high building density, high surface temperature, low moisture and elevation < 30 m classes were extracted and overlayed. The centre of the intersected areas is used to calculate the distance to lakes. These centres, likely representing zones with multiple risk factors (e.g., high building density coupled with low vegetation and high surface temperature), were used to calculate distances to a lake or water body. The calculated distances were incorporated into creating risk maps using a method called inverse distance weighting. The outcome of this process likely produced risk maps that depict spatially explicit information regarding potential risk areas, considering multiple criteria and their spatial relationships.

Results and Discussion

The Building Area and Urban Expansion Analysis

Building density is a measure of urbanization. Figure 2a shows a summary of building density evaluations of the different DSDs over time and percentages of land belonging to the different categories of densities. Noticeable values were reported in 2020, as opposed to other years in all the DSDs in the ‘very high’ density category (i.e., ~31% in Kesbewa, 27% in Maharagama and 23% in Kaduwela). It is important to note that the ‘very high’ category has continuously increased between 2000 and 2020 in all three areas. Maharagama DSD has the highest urban expansion reported for these 20 years followed by Kaduwela and Kesbewa DSD, respectively.

Figure 2.

Building Densities of the Study Areas. (a) Building density variations over time. (b) Building density in Kesbewa-Maharagama 2000–2020. (c) Building density in Kaduwela DSD 2000–2020.

The spatial variability of building densities over time is shown in Figures 2b and 2c. In 2000, building density was higher around the Boralesgamuwa Lake, while most of the remaining areas show moderate levels. By 2015, the building density had increased and spatially moved towards the west of the DSD. The western margin is a mixture of ‘very high’ and ‘high’ building density levels. By 2020, the ‘very high’ building density areas in the westernmost margin had moved to the central part of the Maharagama DSD. In summary, from 2000 to 2015, the surrounding building density had increased, pressurizing the lake environment.

The Kesbewa Lake is located at the centre of the commercial area, and ‘high’ levels of building density were observed around the lake in 2000, with ‘moderate’ levels in other parts of the DSD. Significant changes to density levels were noted in 2010 when ‘very high’ density areas emerged at the western margins of the DSD. These grew further in 2015, and by 2020 moved further into the DSD.

During 2000, the built-up density ranged from ‘moderate’ to ‘high’ levels within the Kaduwela DSD. It was found to be lower on the western side of the DSD, as opposed to a ‘high’ density eastern sphere. The built-up density around the Thalangama Lake was seen as ‘moderate’. By 2015, the surrounding environment of Thalangama Lake had been encroached upon by built-up areas indicating ‘very high’ to ‘high’ levels of building density, albeit there were a few patches of ‘moderate’ building densities on the eastern periphery of the lake.

Vegetation Health Analysis (NDVI)

Figure 3a depicts the different categories of NDVI estimations as percentages of the total land area of the three DSD divisions during different years. Of significance is the gradual increase of the ‘very high’ category observed in all three DSDs from 2000 to 2015. By 2015, 33%, 32% and 29% of land areas were reported as belonging to the ‘very high’ category in Kaduwela, Kesbewa and Maharagama, respectively. After 2015, the vegetated areas seem to be disturbed by the increasing building density (NDBI) apparent by the reduction in the ‘very high’ category in 2020 by 10%, 11% and 12% in Kaduwela, Kesbewa and Maharagama, respectively.

Figure 3.

Vegetation Health of the Study Areas. (a) Vegetation health variations over time. (b) Vegetation health in Kesbewa-Maharagama 2000–2020. (c) Vegetation health in Kaduwela DSD 2000–2020.

Analysis of spatial distributions of NDVI values shows that by 2005 a large extent of low-density vegetation (NDVI range: 0.1–0.5) was spatially distributed within Maharagama DSD surrounding the Boralesgamuwa Lake (Figure 3b). By 2015, the dense vegetation cover (range: 0.3–0.5) had decreased, and the extent of built-up area (0.09 and 0.24) has increased, particularly in the western part. By 2020, almost all the northwestern and western parts of Maharagama DSD (represented by the orange colour tone) were prominently limited to urban/built-up areas. The maximum value of NDVI ranged between 0.3 and 0.5, indicating a moderate value (low density of vegetation), which is now sparse and scattered as patchy tiny plots. The NDVI-based vegetation assessment for the two-decade period clearly expresses that healthy dense vegetation is now difficult to see in the Maharagama DSD, surrounding the Boralesgmuwa Lake in particular.

In the Kesbewa DSD, we find that relatively large patches of vegetation are not distributed except in the northwestern quarter. Most of the areas are confined to shrubs, homesteads and very-low-density vegetation where the canopy cover is low. Therefore, high leaf biomass and healthy vegetation are difficult to find in the DSD. A mixed pattern of vegetation is characterized, which is clear around the Kesbewa Lake environment. The NDVI values classified for the year 2005 ranged from –0.5 to 0.5, where the maximum value expressed low-density vegetation particularly in the northwestern quarter and the central parts of the DSD. By 2010, the maximum value declined to 0.4, indicating a decreased density in vegetation.

More scattered bare rock/sand areas representing minus values and low-density vegetation with low leaf biomass and canopy cover (represented by the classified ‘very high’ category) can be found scattered over the DSD. The lake environment has transformed into a more vegetated area from a previously urban/built-up classification. A linear pattern is seen along the transportation lines; the lake itself seems to work as a node. Three lines seem to extend from the node. These are urban/built-up or simply non-vegetated areas that are more visible in the northwestern quarter of Kesbewa DSD. The contraction of vegetation in Kesbewa DSD as well as around the Kesbewa lake is notable by 2020.

Significant land-use changes are identified in Kaduwela DSD (Figure 3c). Kaduwela DSD is larger in size and population compared to Maharagama and Kesbewa DSDs. By 2005, the maximum NDVI value declined in comparison to 2000, implying a reduction in the density of vegetation cover, canopy cover, and leaf biomass. Except for scattered dark green patches mostly distributed along the boundary of the western sphere, most other parts consisted of patches of shrubs mixed with homestead or urban/built-up areas. Vegetated areas around the lake environment (Thalangama Lake) also declined from the year 2000. By 2010, the vegetation of the Kaduwela DSD illustrated a salient distribution of patches of green within the western and central parts of the DSD. The vegetation distribution in 2015 demonstrates a significant expansion of low-density vegetated areas in almost all the areas except for the western periphery of the DSD. A linear pattern of orange tone could also be identified indicating a linear distribution and horizontal expansion of the areas; the western margin demonstrates deep horizontal expansion. By 2020, the prominent expansion of built-up/urban areas, bare rock and sand areas dominated the landscape. The disappearance of previously vegetated areas is clear with these transformations resulting in, most noticeably, the shrinkage of the Thalangama EPA.

Plant-Water Stress Analysis (NDMI)

Figure 4a depicts the percentage NDMI categories of the three DSDs during the study period. A notable feature is that a cumulative of ~23%, ~19% and 16.6%, the highest within the study period, in the ‘very dry’ and ‘dry’ water stress categories were reported in 2015 in Maharagama, Kesbewa and Kaduwela, respectively. Continuous reduction in the ‘very wet’ category could be seen in Kaduwela and Maharagama, in which a total of 10% and 23% reductions were reported within 20 years.

Figure 4.

Plant Water Stress of the Study Areas. (a) Plant-water stress variations over time. (b) Plant water stress in Kesbewa-Maharagama 2000–2020. (c) Plant water stress in Kaduwela DSD 2000–2020.

Maharagama DSD, which is the smallest DSD of the three study areas, illustrates plant water stress prominently in the western periphery. The areas are categorized under ‘dry’ and ‘very dry’ classes by the NDMI analysis for the year. The spatial expansion of dryness and contraction of wet areas is significant by the year 2005. By 2010, the pixels categorized as ‘very wet’ seem to lean towards the eastern sphere (Figure 4b). By 2015, previously wet areas have transformed into dry areas, and ‘very wet’ regions are confined to the northwestern margin of the DSD. The areas with maximum moisture content seem to be confined to the Boralesgamuwa Lake water body. Almost all the western sphere pixels in the analysed satellite image represent only wet and normally wet categories, indicating sufficient moisture content of plants. The lake surrounding environment plant water stress identified in 2015 has declined and the area is classified in the normally wet and wet class except for two small areas with plant water stress.

Kesbewa DSD illustrates moisture content between ‘wet’ and ‘normally dry’ in 2000. The very wet patches are confined to Kesbewa Lake. Also, very small dry patches are spatially distributed within the DSD. Five years later, the Kesbewa DSD has transformed into insufficient moisturized land. The ‘very wet’ moisture class is confined to narrow tiny patches. By 2010, once again both very wet and very dry patches have spatially increased. The western sphere illustrated extreme plant water stress while the eastern sphere demonstrated extreme moisture content. The normally dry clusters in 2010 have integrated and created large patches of dry areas by 2015 and show distinct differences with 2010. The plant water stress is limited to narrow patches of paddy lands which were classified as very dry areas in 2020.

Kaduwela DSD has moderate to low health vegetation with many disturbances. In 2000, the classified image shows the western sphere of the DSD as having a ‘very dry’ surface leaning towards minus values (Figure 4c). Plant water stress gradually increased during five years into 2005; the loss of moisture content filtered towards the central areas of the DSD. Hence, there were ‘drier’ and ‘low dry’ patches by 2005. By 2010, the extent of dry areas increased while the western border shows the areas with plant water stress. The insufficient moisture content of vegetation and soil has transformed the land area into dry areas which were mainly the result of built-up expansion and contraction of vegetation. The expansion of ‘dry’ and ‘very dry’ areas is evenly distributed within the DSD. By 2015, the areas previously with sufficient moisture content and high biomass were transformed into ‘very dry’ patches. The westernmost border is ‘wet’ and ‘very wet’, whereas areas with built-up land had low or no vegetation reported. These NDMI results are commensurate with NDVI analyses. For example, in 2020, the Thalanagma Lake environment had a ‘very wet’ class NDMI result, indicating sufficient plant water; the NDVI results say ‘high’ to ‘moderate’ levels, demonstrating the vegetation density and canopy cover.

Water Body Change Analysis

Figure 5a shows water body changes within the studied period. Water body area significantly decreased after 2010 in all areas. Within a decade, 26% and 63% of the water body area was reduced in Kesbewa and Maharagama, respectively. Though Kaduwela DSD shows the least change within the latest decade, its extent also reduced by 6%. ‘Very high’ water extent categories varied, while the ‘high’ category oscillated saliently.

Figure 5.

Water Body Area Change of the Study Areas. (a) Water body area changes variations over time. (b) Temporal water body area changes over five consecutive years. (c) Water body area changes over 20 years.

According to the NDWI analysis, the extent of the lake water body has fluctuated over 20 years. The Thalangama Lake, being an EPA, reported the most significant decrease in water extent between the periods 2005–2010 and 2015–2020 (Figure 5b). This becomes clearer when the change is seen from 2000 to 2020 (Figure 5c). Except for Boralesgamuwa Lake, the water extent of Thalangama and Kesbewa lakes has decreased within 20 years. This fluctuation implies the various disturbances that occurred surrounding the respective water bodies.

Temperature Profile and Heat Island Impact Analysis

Figure 6a shows temperature changes within the studied period. ‘Very high’ temperatures are found in 2020 and can be considered as a turning point towards increasing temperature. About 78% of the land area in Mahragama had very high temperatures in 2020, while Kaduwela had 52% of the land area with very high temperatures. It was noted that building density was higher in Maharagama and Kaduwela than in Kesbewa, and the very high LST is attributed to very high building density. Low-temperature areas reduced by > 10% during these two decades. Increasing temperature is further characterized by the concurrent increase of high categories in all areas from 17% to 50%.

LST in Maharagama DSD in 2000 shows ‘very high’ temperature values in the western sphere and the northwestern quarter, especially, and it can be considered an urban heat island. The maximum temperature value recorded was 36.44^oC. Around the Boralesgamuwa Lake, however, the temperature is relatively low ranging between 17.92^oC and 25.58^oC. A salient feature of the temperature distribution in 2010 is that almost all the areas in Maharagama DSD were classified under the ‘very high’ temperature category (from 26.65^oC to 30.78^oC). The Boralegamuwa Lake area is surrounded by a heat island. The second and third heat islands are also detected within the Maharagama DSD. When the LST is compared with building density and vegetation cover, a general relationship can be observed; the higher the building density, the lower the vegetation cover and the higher the LSTs. By 2015, the ‘very high’ temperature category is seen shifting to the western sphere, creating two urban heat islands. Lower to moderate temperature levels are found within the northern and northeastern quarters. The ‘very high’ temperature values drifted towards the eastern periphery of the DSD, creating the second heat island in 2020.

Kesbewa DSD indicated the maximum value; 36.51^oC was recorded for the year 2000, creating urban heat island impacts. Northwestern Island, Central Island and Southern Island are the three major urban heat islands in the Kesbewa DSD. Kesbewa Lake is surrounded by higher to moderate levels of temperature. By 2005, the extent acquired by the ‘very high’ temperature values and heat island impacts increased. Most of the area had low temperatures (from 24.55^oC to 27.09^oC) (Figure 6b). The Kesbewa Lake area had low temperature values mixed with moderate values. The urban heat islands moved far more to the northwest, and the central area. The maximum temperature was 32.43^oC. The ‘very high’ temperature values were significant in 2010. The extent of increased temperature occupied 45.03% of the land. The Kesbewa Lake area temperature gradually seemed to increase. The maximum value was 30.78^oC. Even though the spatial pattern of temperature changed in 2015, the urban heat islands remained as in 2010. However, the LST around Kesbewa Lake reduced in 2010. Thus, the lake area shows moderate to low temperature values, ranging from 21.93^oC to 24.79^oC. By 2020, the urban heat island impact concentration was in the centre of the DSD. The northwestern quarter had lower temperature levels. The maximum temperature was 30.56^oC. The temperature around the Kesbewa Lake area had increased.

LST plays an essential role in the physics of the land surface through the energy cycle and the exchange of water with the atmosphere (Haque et al., 2020). Kaduwela DSD is the most affected DSD with urban expansion and loss of vegetation. The lowest temperature extracted for the year 2000 was 24.25^oC. The maximum temperature was 34.68^oC identified around Battaramulla town, Bomiriya and Oruwla. These areas are the urban heat islands in Kaduwela DSD. In 2005 the impact of LST was limited majorly to the western margin. Battaramulla and adjacent areas are included in the urban heat island. Areas 02 and 03, respectively, are Thalahena and Oruwala. The Thalangama Lake area had mainly moderate temperatures. Surprisingly, the occupied extent related to the ‘very high’ temperature category (from 27.49^oC to 31.65^oC) and had increased by 22.89 sq km) in 2010 compared to previous years. The urban heat island effect has been identified mainly in Battaramulla, Thalahena and Oruwala. The ‘very high’ and ‘high’ temperature levels are increasing around the Thalangama lake. By contrast, the lower temperature values were illustrated mainly in the eastern sphere of the DSD in 2015. However, two urban heat islands with maximum temperature values of 31.63^oC were identified within the eastern sphere. The western sphere is a large urban heat island (Figure 6c). The surface temperature of the lake areas, however, was mixed with moderate values as well. Within five years, in 2020, the urban heat island effect had severely invaded the eastern periphery of the areas. A dramatic change in moderate temperature values was identified. The maximum value was 32.96^oC and the minimum value was relatively lower than in previous years (7.06^oC). Oruwala, Malabe East and Bomiriya are the urban heat islands in the area.

Figure 6.

Land Surface Temperature Change of the Study Areas. (a) Land surface temperature change variations over time. (b) Land surface temperature in Kesbewa-Maharagama 2000–2020. (c) Land surface temperature in Kaduwela DSD 2000–2020.

Multi-criteria Assessment

Multi-criteria assessment using the overlay and interpolation method was used to map environmental risk zones and to define the risk severity areas. Very-high-risk severity indicates low-density vegetation, high LSTs, high plant water stress and high building density. According to the analysis, Thalangama (Figure 7a), Kesbewa (Figure 7b), and Boralesgamuwa (Figure 7c) lakes are at critical disaster levels. Very-high-risk levels are seen positioned around the lake ecosystems. The critical zones extend for several kilometres. The largest critical zone is found around Thalangama Lake.

Figure 7.

Lake Risk Levels: (a) Kaduwela-Thalangama Lake; risk level: very high. (b) Kesbewa-Kesbewa Lake; risk level: very high risk. (c) Boralesgamuwa Lake: Maharagama DSD; risk level: high risk.

When risk levels around the three lakes based on factors such as vegetation, moisture, temperature, building density and water pollution are considered, it is observed that there is a very high environmental risk surrounding the Thalangama and Kesbewa lakes (Figure 7a and b), and high risk at Boralesgamuwa Lake (Figure 7c), suggesting that all the lake locations are vulnerable to environmental damage and degradation. Elevated risk levels have several implications for the lake area communities. Some of the major implications are:

Health risks: Residents living near the lake may be at risk for gastrointestinal problems, cancer, skin and respiratory disorders and other conditions due to water pollution (Wang et al., 2023). Additionally, eating fish and aquatic macrophyte species may have long-term health effects.

Loss of tourism: Pollution and degradation of the lake and its environs can harm the tourism sector, especially local tourism, which can result in lost income and jobs for the community.

Decline in property prices: A lake area that has suffered environmental degradation can have a detrimental effect on local property values, causing individuals and companies to suffer significant financial losses. Although the area has been developed as a tourism destination, local informants claim that the dirty lake environment has impacted visitor attitudes.

Threat to biodiversity: The high-risk areas surrounding the lake have the potential to seriously harm the local ecosystem and biodiversity, which could result in the extinction of plant and animal habitats. Many birds that once lived around lakes are now tainted by toxic water and aquatic life. The food sources, nesting locations and general health of many bird species may be in jeopardy due to these circumstances.

Public safety: Areas near lakes that are extremely high risk may be more vulnerable to natural disasters like floods. Previous research has made it clear that periodic bouts of heavy and frequent rainfall have produced medium- to high-level floods a few times a year.

Detrimental effects on quality of life: The high-risk area surrounding the lake may harm the quality of life of the local population by raising stress levels and limiting their access to parks, bodies of water and leisure activities.

The local municipal council is not the only entity that must bear the financial burden of implementing measures that enhance the ecological status of the lake environment. This can also be accomplished by other stakeholders and beneficiaries. Money can come from two sources: (a) wealthy locals who recognize the risks of living close to the lake and take precautions to keep themselves and their families safe; and (b) businesses in the area that operate, particularly those that depend on tourism, and can use this information to understand how the ecological health of the lake can affect their business operations and revenue.

Implications of This Study on Sri Lankan Environmental Policies

The risk-zone findings may have a big impact on the environmental governance and policies of Sri Lanka. The preservation and safeguarding of the country’s natural resources is already a priority of Sri Lanka’s environmental policy framework. This study’s findings may have a variety of positive effects on the policies that are in place, particularly in the Colombo Metropolitan region. The government can use these findings to guide a precautionary approach to environmental management. By proactively conserving high-risk areas, the Sri Lankan government can prevent environmental damage, ensure sustainability, and protect public health through:

Creation of focused conservation measures: The government can identify high-risk locations needing immediate conservation actions by incorporating these findings into current regulations. Environmental organizations and legislators can create targeted strategies to address identified hazards. These data can guide zoning and land-use planners in making informed decisions regarding the lake area, identifying regions for development, conservation and urgent pollution reduction.

Creation of inter-agency partnerships: Incorporating these findings into policies can foster partnerships between various departments and agencies handling environmental management. Data can facilitate collaboration and information exchange, helping manage environmental concerns effectively.

Fines and penalties for environmental damage: The government can impose fines and penalties on businesses and individuals that harm high-risk areas near the lake, such as through a ‘polluter pay’ policy. This can discourage activities that endanger the environment.

Local community and municipal planner actions: Local communities and municipal planners must take proactive measures to alleviate environmental degradation around the lake. Discussions with local populations and studying global use cases can identify actions to preserve lake ecosystems and ensure long-term sustainability. Restoration strategies should be tailored to each lake’s unique features and challenges, as in Thalangama Lake. Collaborating with regional authorities on lake ecology and conservation ensures optimal restoration techniques.

Improving water quality: Reduce pollution reaching lakes by upgrading wastewater treatment facilities, tightening industrial waste management laws and promoting public education on proper waste disposal.

Vegetation management: Control invasive plant species, such as water hyacinths, to maintain the ecosystem’s balance and support native vegetation growth. Implement practical management plans for invasive species through consistent observation.

Restoration of wetlands: Recognize the ecological importance of wetlands in maintaining lake health and restore damaged or destroyed wetland areas. This may involve replanting native species, re-establishing natural water flow patterns and creating habitat structures to support aquatic biodiversity.

Watershed management: Implement comprehensive watershed management strategies, including run-off and erosion control, and land-use planning. Prevent excessive urban expansion, deforestation and soil erosion in surrounding areas to improve lake health.

Protecting the shoreline: Implement shoreline stabilization techniques, such as riparian plantings, buffer zones and erosion control structures. This will preserve habitats, prevent soil erosion and maintain water quality.

Sustainable fisheries: Enforce sustainable fishing practices, including fishing seasons, catch size limits and ethical methods. This will maintain fish population sustainability and balance recreational activities.

Education and community involvement: Educate the local community on the importance of lake conservation and involve them in initiatives. Conduct workshops, awareness campaigns and educational programmes to promote responsible environmental behaviours.

Frequent monitoring and evaluation: Establish long-term monitoring programmes to assess lake conditions and restoration success. Frequent monitoring allows for adaptive management, ensuring restoration plans can be adjusted as needed.

Cooperation and partnerships: Encourage cooperation between governmental organizations, local communities, NGOs and other stakeholders to create a coordinated approach to lake restoration and conservation. Coordination, knowledge sharing and resource pooling can significantly enhance restoration project outcomes.

By incorporating these findings into current legislation, the government can better govern environmental governance, safeguard natural resources and improve public health by regulating land-use planning, reducing pollution and encouraging sustainable development in high-risk areas surrounding the lake.

Limitations of the Study

Geospatial data-driven risk assessment methodologies, as employed in this study, have various limitations, including (a) data quality and availability, (b) parameter sensitivity, (c) generalization and subjectivity in classification, (d) methodological constraints and (e) temporal changes and dynamics. Even when photographs with less than 10% cloud cover are used, residual cloud cover or atmospheric disturbances can have an impact on data accuracy. Using photographs at five-year intervals may overlook short-term changes or seasonal variations that are important in understanding risk patterns. The 30 m spatial resolution may not catch finer details, particularly in urban regions with diverse environments.

Indices such as NDVI, NDWI and NDBI are subject to atmospheric conditions, sensor calibration and land surface features, which can result in mistakes in estimated values. Indices like NDVI presume consistent reflectance qualities, which may not be true across different vegetation kinds or phases of growth, which were not addressed in this study. The process of reclassifying raster data into five danger levels involves subjective decisions about threshold values, which can have an impact on the findings. Classification into discrete risk levels may oversimplify complex environmental and socio-economic systems, thereby overlooking nuanced risk changes.

This interpolation method implies a smooth gradient between known data points, which may not adequately reflect the actual distribution of environmental dangers. The selection and relative importance of several variables (e.g., vegetation density, building density and surface temperature) are prone to researcher bias and may fail to capture all important risk factors. The study gives data snapshots at various moments in time, which may not fully account for long-term dynamic changes or trends. Some environmental changes may have delayed consequences that are not immediately noticeable in the data intervals used. These constraints underscore the significance of carefully interpreting geospatial data-driven risk assessments, as well as using different data sources, methods and ground validation to improve accuracy and reliability.

Conclusion

The research conducted on the vulnerable locations surrounding the lakes highlights the critical need for proactive measures to safeguard the environment while promoting economic development. This study underscores the importance of sustainable development, which seeks to balance environmental preservation with economic growth, especially in developing nations like Sri Lanka. By identifying high-risk areas and understanding the threats to environmental sustainability, decision-makers can implement policies and initiatives aimed at mitigating these risks. This approach is vital in ensuring that economic progress does not come at the expense of environmental degradation. Further, the study underscores the critical role of data-driven decision-making in effective environmental management. Without comprehensive data and risk analysis, it becomes challenging to design targeted conservation measures to safeguard natural resources effectively. Therefore, the findings from the lake risk-zone study provide valuable insights for policymakers to make well-informed decisions regarding conservation and environmental management. By utilizing data-driven approaches, policymakers can identify high-risk areas, assess the extent of environmental threats, and prioritize conservation efforts accordingly. This enables more effective allocation of resources and implementation of strategies aimed at mitigating environmental risks. Prioritizing environmental sustainability is not just a moral imperative; it is also a strategic necessity for achieving long-term prosperity and well-being. By embracing sustainable development methods, policymakers in developing nations like Sri Lanka can pave the way for a more resilient, equitable and prosperous future for all.

Footnotes

Acknowledgements

The authors thank N. A. U. S. Senevirathne of the Central Environmental Authority for providing information related to EPA laws and regulations and W. S. Harshana of the Urban Development Authority for supporting in mapping.

Author Contributions

M. D. K. Lakmali Gunathilaka: Conceptualization, methodology, investigation, formal analysis and mapping, writing the original manuscript. Lasantha Manawadu: Supervision, resources. Dinuke Munasinghe: Manuscript review, editing and restructuring. Devanmini Halwathura: Principal supervisor, conceptualization, review and editing the manuscript.

Declaration of Conflicting Interests

The authors declare no competing financial interests. The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors declare that no funds, grants or other support were received during the preparation of this article.

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