Introduction

According to the Millennium Ecosystem Assessment report (2005), the global extent of wetlands is estimated to be on around 1280 million hectares (ha) and these wetlands are under severe threats of degradation due to various reasons. The wetlands comprise of the marsh, fen, peatland or water, whether natural or artificial, permanent or temporary, with water that is static or flowing, fresh, brackish or salt, including areas of marine water the depth of which at low tide does not exceed 6 m (Ramsar Convention 2010). The loss of wetland is, in fact, faster than any other ecosystem all over the globe. These changes are reflected in areal shrinkages, growth of aquatic vegetation or weeds and water pollution or change in water properties. Considering the immense role of wetlands in ecosystem protection and livelihood of people, it is imperative to generate information for their protection and management. Long-term change provides a better understanding of trends and dynamics of wetlands which enables the management agencies to prepare restoration or conservation programs. The strategic goal 2 of the Ramsar Convention’s fourth Strategic Plan (2016–2024) emphasizes effective conservation and integrated management of Ramsar sites. Its mission advocates wise use of wetland by all the stakeholders to achieve sustainable development goals (SDGs) (Ramsar Handbook 2016). The critical role of wetlands in achieving SDGs cannot be ignored due to multiple benefits and services associated with it for people as well as for nature. Wetlands support biodiversity, provide water supply, support agricultural activities, regulate climate, recharge groundwater, provide flood and coasted protection, offer opportunities of recreation, tourism and livelihood (Bassi et al. 2014).

In India, inventories on wetland have been generated in different years. The first wetland inventory for India was prepared by Space Application Centre on 1:250,000 scale using Indian Remote Sensing satellite (IRS) data in 1992–93. The inventory reported the wetland extent at 8.26 million ha of land. This inventory was updated in 2004–05 (SAC 2011; Panigrahi et al. 2012). The National Wetland Atlas prepared by Space Application Centre (SAC) in 2011 reported on the extent of wetlands at 15.26 million ha, which was 4.64% of the total geographical area of the country. The Atlas followed a three-tiered classification, and a total of 19 classes of wetlands were identified (SAC 2011). The land use/land cover categories reported by the Atlas was waterbody and vegetation. Another feature of the Atlas was its pre- and post-monsoon analysis. Seenivasan (2013) emphasized the need to undertake a study of past and present status of wetlands in order to bring out status change in their areal extent.

This study has attempted to find out the long-term change in the selected wetland of Punjab using satellite images. The present study, therefore, seeks: (1) to prepare land use/land cover inventory of three wetlands (Harike, Nangal, and Roper) in Punjab by delineating maps and provide the estimates of different classes; (2) to document surface water dynamics of three wetlands from 1990/1991 to 2018 through manual digitization; and (3) to evaluate the performance of two band indices (NDWI and MNDWI) in mapping and monitoring waterbodies and assess the accuracy.

Remote Sensing and Wetland Mapping

In order to protect wetland from degradation, there is a compelling need to monitor the status of wetlands and generate accurate inventories on the spatial extent and monitor the temporal changes. According to Gallant (2015), monitoring of wetland in situ is very expensive especially when the study intends to capture information on regional, national and global scale. Hence, remote sensing technology can provide opportunities to map wetland at different scales (Prigent et al. 2001; Ozesmi and Bauer 2002). A review paper published by Guo et al. (2017) provides an overview of wetland remote sensing research conducted between 1964 and 2017 and highlights different data and methods used in wetland research. Their study proposed the uses of remote sensing in future wetland research with consideration of scale effects.

Remote sensing is inexpensive and can provide cost-effective information on a broader scale at an economic rate and its archival data offers scope for temporal change detection of wetland (Adam et al. 2010). However, mapping and monitoring wetland using remote sensing create difficulties due to the various characteristic of wetlands (Fig. 1 ). Many wetlands are seasonal and mapping requires selection of data on the basis of seasons. Wetland may cover open water and aquatic vegetation which affect the reflectance properties. Studies also highlight that the land use/cover and management practices affect the quality of water and types of aquatic vegetation in the wetland. Remote sensing data with different spatial resolutions have been used for mapping and monitoring wetland. Various studies have used MODIS (moderate resolution imaging spectroradiometers) data for mapping of the waterbody inundation (Mizuochi et al. 2014; O’Grady et al. 2014) and wetland area monitoring (Petus et al. 2013; Chen et al. 2014). Landsat data has been widely used in wetland mapping (Mertes et al. 1993; Son et al. 2015). SAR (synthetic aperture radar) data has also been used, in a combination with Landsat data, to track seasonal changes in wetland vegetation and surface water spread (Gallant 2014). Studies are also available on land cover types and water quality to determine how land cover changes affect the environment (Choi and Han 2013). Mapping and monitoring the wetland and its associated land uses are crucial to understand its dynamics and prepare conservational plans. Remote sensing data provides accurate information on encroachment of wetland areas by other activities and advances our understanding of reasons for loss of wetland.

Fig. 1
figure 1

Selected wetlands of Punjab are located on Landsat 8 image of 2018 (upper section). Mapping of surface waterbody is easy through remote sensing data; however, the presence of vegetation with water and marsh creates difficulty in mapping (field photo, 2018)

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Study Area

A study carried out by Ladhar (2002) had reported 12 natural wetlands (8.39 km2 in area) and 9 human-made wetlands (147.39 km2 in area) in Punjab. The National Wetland Atlas (2011) mapped 1381 wetlands in Punjab which covered 86,283 ha (including 5049 small wetlands) which accounted for 1.71% of the total geographical area of the state (SAC 2011). The major category occupying 69% of the wetland is rivers and streams, 14% is occupied by reservoir and barrages while remaining 17% are in the forms of tank/pond, waterlogged, lake and riverine wetland. Punjab is an important food grain producing state of India and the role of wetlands as sources of irrigation, hydropower generation and as shelters for varieties of birds cannot be ignored. Due to their immense role in biodiversity protection, three wetlands (Harike, Roper, and Kanjli) are included in the Ramsar list and many more are declared wetlands of national importance. Figure 2 shows location of Punjab in India and the distribution of different wetlands in the state.

Fig. 2
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Distribution of important human-made and natural wetland in Punjab (Map prepared using Google Earth)

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The largest human-made wetland of northern India, the Harike wetland, came into existence in 1952 after the construction of reservoirs at the confluence point of River Sutlej and Beas and lies at 31° 05′ 15″ North to 31° 14′ 15″ North and 74° 55′ 30″ East to 75° 07′ 30″ East longitude (Chopra et al. 2001). The Harike wetland covers an area of 7406 ha (SAC 2011). In 1981, it was declared a wildlife sanctuary by the state government (Ladhar 2002; Tiwana et al. 2008). The Ropar wetland is located in the eastern part of Punjab at 30° 57′ North to 31° 06′ 30″ North latitude and 76° 25′ 30″ East to 76° 36′ East longitudes and is situated at an elevation of 275 m above the mean sea level (Verma et al. 1998). It covers an area of 1365 ha including 800 ha under water (ENVIS 2015). The Nangal wetland came into existence on the Sutlej River in 1961. As for its geographical location, the wetland is situated between 31° 22′ North to 31° 37′ North latitudes and 76° 23′ East to 76° 38′ East longitudes. The water from Nangal wetland is used for power generation and irrigation purposes. As per the notification of Department of Forest and Wildlife Preservation, the Nangal wetland is expanded over 715.83 acres (289.53 ha) of area (Government of Punjab 2009). All the three wetlands are important sources of fresh water, and serve as habitats to a number of migratory birds and are sites for recreational activities.

Several studies have reported the degrading conditions of the wetlands of Punjab and other states of India. Harike, a wetland of international importance, is found to face problems of siltation, decreasing trend of flow, weed infestation (Ladhar 2002; Jain et al. 2008; Jain et al. 2008; Tiwana et al. 2008; Kumar and Kaur 2018) pollution, declining water quality due to industrial waste, presence of heavy metals (Kumar et al. 2016; Brraich and Jangu 2016; Mabwoga et al. 2010) and encroachment due to agricultural activities (Chopra et al. 2001). Due to dependency over wetland for agricultural activities and requirement of land for many developmental activities, the wetlands are highly vulnerable to change in land use and conversion (Chitra 2016). The threats identified for Ropar wetland are industrial expansion, indiscriminate use of agricultural land, human interferences and recreational activities (Verma et al. 1998). Nangal wetland is facing the problem of siltation, weed infestation and agricultural runoff.

Materials and Methods

The data used in the study are Landsat 5, Landsat 7, Landsat 8 of 30 m spatial resolution and IRS LISS IV data of 5.8 m spatial resolution. All the Landsat data with a span of 28 years from 1990/1991 to 2018 are downloaded from the website of the US Geological Survey (USGS-http://www.usgs.gov). The IRS LISS IV data were procured from the National Remote Sensing Centre, Hyderabad. Table 1 shows the details of the satellite data used for the study.

Table 1 Satellite images used in this study
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The most common method used in the extraction of the water surface is two band indices (Ryu, Won and Min 2002). Many studies have shown that variables such as the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference built-up index (NDBI) are useful for wetland classification (Rouse Jr. et al. 1974; Allen 2012; McFeeters 1996; Xu 2006; Rokni et al. 2014; Zha et al. 2003; Chen et al. 2006). Studies have also used the method of density slicing of mid-infrared band (Johnston and Barson 1993), land surface water index (LSWI) (Dong et al. 2014), maximum likelihood supervised classification (Zhang et al.  2011), object-oriented classification method (Jia et al. 2013, 2014), visual interpretation method (Xue et al. 2018) and Tasseled-cap Wetness index (TWI) (Ndayisaba et al. 2017) to monitor wetland. The normalized difference water index (NDWI) is a widely used method to delineate open water features because waterbodies appear very distinct in visible and infrared wavelength due to their strong absorbability (Ashtekar et al. 2019; Yan et al. 2017; Bahera et al. 2012). The NDWI makes use of visible green light and reflected near-infrared radiation (NIR) to enhance the presence of waterbody. The method of NDWI (Green − NIR/Green + NIR) was designed by McFeeters in 1996 to detect surface waters in wetland environments and to allow for the measurement of surface water extent. He classified the positive value above zero as waterbody and all the negative values as non-water. Another index used is MNDWI (modified normalized difference water index) which uses green and short wave infrared (SWIR) bands for extraction of water features (MNDWI = Green − SWIR/Green + SWIR). Studies have shown that the built-up features also produce positive values in NDWI and the water features are mixed up with built-up noise and affect the accuracy of the result. The MNDWI enhances the water feature results especially of open water features and the built-up land, soil and vegetation come up with negative values and are nearly suppressed or even removed (Xu 2006). Several studies have reported that long-term change detection can enable researchers to better understand the trends and dynamics of wetlands and can assist in preparing protection strategies (Guo et al. 2017). The present study has generated NDWI of three wetlands of Punjab for three different years 1990/1991, 2005 and 2018 and MNDWI for 1990/1991 and 2018. These two methods are compared for two different periods: 1990/1991 and 2018. Another widely adopted method to quantify wetland change is land use/land cover classification and change detection (Chopra et al. 2001; Douglas et al. 2018; Widney et al. 2018). Visual interpretation of land use features focussing water feature is performed for 1990, 2005 and 2018 for Harike wetland. The land use/land cover maps for Ropar and Nangal wetlands and their surroundings are created through visual interpretation for the year 2018. Thus, water features are extracted using three different methods: visual interpretation, NDWI, and MNDWI. The results obtained through the two band indices are compared and evaluated with pixel-by-pixel comparison using the reference images and accuracy is assessed.

It should be noted as limitation of the study that the atmospheric correction of images has not been done and the minimum, maximum and optimum threshold values may differ from the corrected images. The spatial resolution of the Landsat data used in the study is 30 m and the result at edge pixels can cause underestimation of the result for the waterbody. In order to perform the indices, a large section covering wetlands are subset from the image. The purpose of performing two band indices is to assess the accuracy of the result generated by these two. The larger area represents the wetland and their surrounding and therefore, the area of waterbody generated through two band indices is likely to vary from the total area of the wetland boundaries.

Results

Land Use/Cover Dynamics in Wetlands

Figure 3 shows the land use/cover categories of Harike wetland in three different years. The wetland has experienced significant changes due to influences of human activities inside the sanctuary area during the past 28 years (Table 2). The dominant category was waterbody in 1990 while the cultivated land covered 2% of the total area. Over 28 years, the area under cultivated land has increased while the area under waterbody (stagnant water in the form of the lake) has significantly decreased within the sanctuary area from 3114.60 ha in 1990 to 1330.41 ha in 2018. Area under shrub and marshy land has also decreased (see Table 2 for change in different years). The decrease is very conspicuous in southern and central parts where open waterbody is covered by aquatic plants like hyacinth (Fig. 3). The entire wetland is very fertile and expansion of cultivated land can be seen in the northern part and along river Sutlej in the southern part. Encroachment inside sanctuary area for cultivation and grazing activities are considered major obstacles in management of the wetland. Land use/cover maps for Ropar and Nangal wetlands are prepared for the year 2018. The land use has been categorized into seven classes for Ropar wetland and four classes for Nangal wetland (Fig. 4). The area under each class is given in Tables 3 and 4. The major portion of these two wetlands is covered by water, and therefore, the land use/cover was prepared in and around the wetlands. This is done because the land use practices in surrounding environment can affect the quality of wetlands. The land use/cover categories for all the three wetlands vary due to different activities observed in and around of the wetlands. For example, agricultural land is a common land use category in all the three wetlands while industrial activities are observed only around Ropar wetland. Much changes are not reported in land use/cover categories of the wetlands, and therefore, the change detection was not performed.

Fig. 3
figure 3

Land use/cover change inside the Harike sanctuary area. March, 1990 (a), March 2005 (b), March 2018 (c)

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Table 2 Aerial estimates of land use/cover classes in Harike Wetland (Areas in hectare)
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Fig. 4
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Land use/cover around Nangal Wetland, 2018 (a) and Ropar Wetland, 2018 (b)

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Table 3 Aerial estimates of land use/cover classes in Ropar wetland and surrounding (2018)
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Table 4 Aerial estimates of land use/cover classes in Nangal wetland and surrounding (2018)
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Surface Water Dynamics of Wetlands

The Harike sanctuary area comprises of a section of river Beas, river Sutlej and lakes. Figure 5a shows the surface water dynamics of Harike wetland from 1990 to 2018. In a span of 28 years, severe changes have occurred inside the sanctuary area. Fluctuations in the course of Beas and Sutlej rivers can also be seen. Shrinkage in surface water cover has occurred from 1990 to 2005 due to growth of hyacinth and other aquatic weeds in the lakes (in the south, between eastern branch of river Beas and river Sutlej and between two branches of river Beas. In 2002, a cleanliness drive was started by the state government with the help of Indian Army to remove aquatic weeds especially hyacinth (Kumar 2019). This resulted in an increase in the area under surface water after 2002. Though the area of surface waterbody has declined, the result of cleanliness drive can be seen in the south-eastern part of the sanctuary. In between 2015 and 2018, surface waterbody again increased marginally in the central lake.

Fig. 5
figure 5

Surface water dynamics of Harike wetland (a); Nangal Wetland (b); Ropar wetland (c)

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Nangal and Roper wetland are located in hilly areas and are of small spatial extent. Contrary to the observation of Harike wetland, areas under surface water bodies in the Ropar and Nangal wetlands have reported marginal status change from 1990/1991 to 2018. This study reports that the water surface in Nangal wetland has declined by 13.7 ha from 248.6 ha to 234.9 ha from 1990 to 2018. From Fig. 5, it is observed that very few changes have occurred in the northeast direction of Ropar wetland and its surface waterbody extent has increased from 413.8 ha in 1990 to 426.4 ha in 2018.

Two Band Indices for Water Body Extraction: NDWI and MNDWI

The NDWI images have been generated for the Harike, Ropar and Nangal Wetlands for 1990/1991, 2005 and 2018. The result is presented in a color-coded image with only two categories: surface water body and the other/non-waterbody. The index value of NDWI of Harike wetland ranges from − 0.486 to 0.202. The positive value above 0 shows the waterbody. The lowest value corresponds with the agricultural land. Marshy land and other non-waterbody show negative values. The MNDWI value for Harike wetland ranges from − 0.266 to 0.290, and the value above 0 shows the waterbody. The area computed for waterbody through MNDWI is found to be higher than that derived from NDWI. Waterbody covered under weeds and marshy land also showed positive value in MNDWI. The NDWI and MNDWI values for Nangal wetland range from − 0.432 to 0.145 and − 0.381 to 0.225, respectively, and in both the indices the results for water body and non-waterbody are similar. The NDWI and MNDWI values for Ropar wetland range from − 452 to 0.115 and − 0.290 to 0.220, respectively, and MNDWI gives better result than NDWI. The output of waterbody extraction from NDWI for three wetlands is presented in Fig. 6.

Fig. 6
figure 6

a Landsat image of Harike wetland and color-coded NDWI image of 1990 (top), 2005 (middle), 2018 (bottom). b Landsat image of Nangal wetland and its surrounding and Color-coded NDWI image of 1990 (top), 2005 (middle), 2018 (bottom). c Landsat image of Ropar wetland and its surrounding and color-coded NDWI image of 1991 (top), 2005

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The NDWI and MNDWI images both, of all the three wetlands, clearly show open water features as the result of enhancement (Fig. 7). Nevertheless, the MNDWI produced a better result as compared to NDWI. The NDWI gives inconsistent result at various places: The waterbody gives negative value and the bare soil value is found to be similar with waterbody value at many places. While these inconsistent values are quite stable in the MNDWI suggesting that the errors present in the NDWI can be removed or suppressed with the use of SWIR band (Li et al. 2013; Singh et al. 2015). In the case of Nangal and Roper wetlands and their surroundings, the MNDWI presents more contrasting result by suppressing the reflectance values of other features. However, the difference in water surface area was not significant in both the images. Table 5 shows the areal change in waterbody derived from two indices between 1990/1991 and 2018 (Fig. 8).

Fig. 7
figure 7

Comparison of waterbody extraction from MNDWI at three wetland sites. a Harike wetland in 1990; b Harike wetland in 2018; c Roper wetland and its surrounding in 1991; d Roper wetland and its surrounding in 2018; e Nangal wetland and its surrounding, 1990; f Nangal wetland and its surrounding, 2018. The threshold value is set at 0

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Table 5 Change in the area under waterbody in Harike, Ropar and Nangal wetlands based on double band indices (Areas in hectares)
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Fig. 8
figure 8

Comparison of waterbody shown in NDWI and MNDWI stretched images at three wetland sites. a NDWI image of Harike wetland; b MNDWI image of Harike wetland; c NDWI image of Roper wetland and its surrounding; d MNDWI image of Roper wetland and its surrounding; e NDWI image of Nangal wetland and its surrounding; f MNDWI image of Nangal wetland and its surrounding. All the images are for year 2018

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The validity of the result obtained through two band indices is tested through accuracy assessment. The stretched images are classified using threshold value into two categories: water and non-waterbody. Campbell (2007) has suggested using evidence to test the validity of the result. The accuracy of both the images was assessed using 60 random samples points for water/non-waterbody categories in ERDAS Imagine 2014 software. The reference data used to check accuracy for the year 2018 images is high spatial resolution Google Earth image of 2018 while multispectral images are used for the year 1990/1991 as reference data. The kappa coefficient for NDWI image is 0.8036 and 0.8567 and for MNDWI image is 0.8567 and 0.9495 for the years 1990/1991 and 2018, respectively.

Discussion

Different methods were employed to create land use/cover and to work out surface water spread in wetlands. When the area is small, the classes can be created by manual digitization through visual interpretation. Manual digitisation of smaller areas gives good results provided it is done very patiently over high resolution data; but, of course, is time consuming. Digital methods are a better alternative especially when the areas are complicated and large. The study found that there are conspicuous changes in the land use pattern of Harike Wetland. The area under waterbody, derived through visual interpretation, has declined from 42% (3114.6 ha) in 1990 to 18% (1330.4 ha) in 2018. The extent of other land uses has increased from 5 to 82% during the same period. The decline in water level in Harike wetland is due to weed infestation and human interferences for agricultural activities. The MNDWI result suggests that the area under waterbody in Harike wetland has declined from 3182 ha in 1990 to 1809.38 ha in 2018. In addition, areas under the waterbody decreased in the southern, central and northern regions.

The NDWI and MNDWI for Ropar wetlands and its surroundings show little changes because the level of water is maintained by the construction of headworks. Similarly, very few changes occurred in the surface waterbody of Nangal wetland and its surroundings due to the nature of wetland as a result of dam construction. A fixed amount of water in Nangal wetland is released from the Bhakra Dam and a fixed amount of water is discharged into Nangal hydel and Anandpur Sahib hydel channels from Nangal wetland (Kumar 2019). The study shows that the wetland located in hilly areas for the purpose of hydel power generation has little fluctuation in water surface while the Harike wetland located in plain area has suffered major fluctuation in the extent of water spread. The MNDWI result shows that the surface waterbody of Ropar wetland and its surroundings has increased from 790.6 to 852.89 ha while it has decreased from 366 ha in 1990 to 348.18 ha in 2018 in Nangal wetland and its surroundings.