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Assessment of decadal land use dynamics of upper catchment area of Narmada River, the lifeline of Central India
⁎Corresponding author. tarun.thakur@igntu.ac.in (Tarun Kumar Thakur)
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Received: ,
Accepted: ,
This article was originally published by Elsevier and was migrated to Scientific Scholar after the change of Publisher.
Peer review under responsibility of King Saud University.
Abstract
India is a land of rivers and Narmada is one of the principal river systems and is ascribed as the lifeline of Central India. Freshwater ecosystems such as rivers across the globe are facing degradation due to multitude of anthropogenic stress factors. Holistic and sustainable approach is prerequisite for monitoring, risk assessment and management of such multitude of problems and critical challenges. Geographical Information System (GIS) has emerged as a powerful tool for carrying out scientific and unbiased monitoring and assessment studies as well as understanding the degradation of ecosystems. The present study focuses on decadal land use changes along the upper catchment area of Narmada river basin. Vegetation was spatially analyzed for digitally classifying numerous imageries using the maximum likelihood algorithm (MLA). Six land cover types were identified which includes dense mixed forest, Sal dominated forests, barren landscapes, agricultural fields, water bodies as well as habitation and commercial spaces. The vegetation structure and species composition are important ecological attributes of the ecosystem. Our study area has faced intensification in anthropogenic stress factors, which is observable in our temporal variations study as well. Increasing urbanization and deforestation in river valley regions is alarming and testimony of the fact. Therefore, in order to maintain the river health advocacy at policy level is needed. The current study is an attempt in that direction. It is therefore essential in developing a road-map for sustainable development of this important riparian ecosystem.
Keywords
Freshwater ecosystems
GIS
Ecology
Anthropogenic stress
Sustainable development
1 Introduction
The sustainable development goals (SDGs) of 2030 as adopted by United Nation member states set up by United Nations General Assembly are blueprint of better and sustainable future for all. Several countries are working hard to achieve the shared vision of development to the best of their abilities. The different aspects of various goals like 6, 11, 12, 13 and 15 have been clubbed together in this research project to accomplish the aim enumerated in goal 15. It explains to “protect, restore and promote sustainable use of terrestrial ecosystems, sustainable management of forests, combat desertification, halt and reverse land degradation and halt biodiversity loss”. Meeting the objectives of SDGs is pivotal in order to comprehensively address the costs associated with anthropogenic impacts and ecosystem trade-offs. Some of the major cities across the globe that they have rapidly altered the land use and land cover (LULC) dynamics to achieve economic goals and urbanization in order to attain the needs of a rapidly growing population (Barros, 2004). The LULC changes study plays a crucial regulatory role in changing total atmosphere and the environment (Qian et al., 2007). Land use patterns denote to the physical features of the earth’s surface like distribution of green vegetation, water, soil and other physical land features generated through anthropogenic activities. Thus, addressing SDG Goal 11 that is to make the urban centres and human settlements more inclusive, safe, resilient and sustainable through this initiative is pivotal. The quantitative analysis of the changes occurred through LULC is crucial to study the corresponding impact on the ecosystem service value (ESV). It could help in the decision-making process for the sustainable development of ecosystem (Lin et al., 2018). The alternations in different component of terrestrial ecosystems could be estimated and monitor through LULC (affecting the different components of the ecosystem directly or indirectly (Guzha et al., 2018). The last century has seen many natural disasters and climate changes across the globe like rise in sea surface temperature, melting of polar ice, sea level rise, change in global wind circulation & ocean currents and weather changes. This has caused detrimental impacts on the biodiversity around the world. Continued overexploitation of natural resources leads to serious threat and it represents unsustainable consumption and production patterns which hampers the Goal 12 of the SDG and adversely discourage the sustainable consumption and production patterns. Its adverse effect can be easily observed in the form of degrading freshwater ecosystems. Preserving freshwater resources for both people and wildlife is essential and is a top priority responsibility to protect the planet. In this manuscript, we have used recent scientific advance techniques to ensure sustainable and holistic development of the upper reaches of Narmada flowing across Achanakmaar Amarkantak Biosphere Reserve (AABR). In order to mitigate the effect of climate change and adapt it as per the fulfillment of goal 13 of the SDG, availability of credential data is crucial. The United Nations have recognized the inextricable link between water and climate change by making it a theme for the World Water Day of 2020. This initiative explains and emphasizes the importance of water and its potential role to mitigate the effect of climate change. Further, it urges the international community to keep water conservation at the top priority during designing the climate related policy. Protection and conservation of freshwater resources such as rivers, streams, springs and lakes distributed across the globe is the need of the time and this manuscript proposes a sound scientific data set for policy makers. It argues that, a land use land cover map of a drainage basin provides a holistic view of the river actual health conditions and also the natural and anthropogenic stress factors that impacts the river sustenance. Under such circumstances, development of the Digital Elevation Model (DEM) is crucial. The resolution of the satellite image varies with the sensors, but the DEM provides the foundations for extracting data pertaining to the drainage network. Many critical hydrological phenomena such as the size, shape and slope of the drainage area, the drainage density, the size and length of the tributaries, etc., correlate with the physiographic characteristics of drainage basins (Rastogi and Sharma, 1976). The aforementioned techniques and tools were chosen for the generation of the current scientific data set. Few limitations of LULC have been noted in hydrological modeling and evapotranspiration estimations where little or no information on the temporal dynamics of LULC classes have been reported (DeFries et al., 1999). AVHRR pathfinder time-series images have become cutting-edge procedures to capture temporal dynamics of LULC at global level (De Fries et al., 1998; Loveland et al., 2002). LULC studies of drainage basins are particularly important in case of rain-fed river systems such as Narmada. It is prone to flooding with prolonged monsoonal rains and erratic precipitation pattern under current climate change scenario (Guhathakurta, et al., 2011; Kulkarni et al., 2013). LULC analysis confirmed around 74.84% increase in built up area and 42.8% decrease in open spaces area during the years 1966–2009 due to increase in the degree of urbanization over the decades in Oshiwara river basin (Zope, et al., 2016). LULC change detection studies have profound impacts on hydrological processes and have been investigated prominently in the recent times (Fox et al., 2012).
The current study attempts to investigate the decadal changes in land use and land cover along upper catchment basin area of Narmada. The primary objective of the current research is to understand the decadal land use changes as well as to identify the anthropogenic stress factors of the study area. The vegetation mapping also ensured the study of phytodiversity of the area. DEM have been constructed for hydrological mapping and geomorphological analysis of the study area.
2 Materials and methods
A workflow of methodology for the current monitoring and assessment analysis is described in Fig. 1. The flow chart is self-explanatory and discusses the various stages of the analysis work carried out during the current research.
Methodology workflow and data analysis.
2.1 Study area
Upper catchment area of Narmada River was selected as the study site which is a part of Achanakmaar Amarkantak Biosphere Reserve (AABR), Central India. The study area endowed with biodiversity, medicinal, herbal and aromatic plants. There are many minor rivers of this region such as Gayatri, Savitri, Kapila, Baitarini, Arandi, Arpa, Bakan, Tipan and Karmandal. These small rivers provide a natural source of water throughout the year to the Narmada river. Besides minor rivers, many perennial streams and wetlands are found which supply the water to main stream of Narmada.
The Narmada river system originates from Amarkantak highlands located between the map coordinates 22°15′ N to 22°58′ N and 81°25′ E to 82°5′ E covering a geographical area of approximately 35.61 km2 at a mean altitude of 1048 m from average sea level. The base map is demonstrated in Fig. 2. The climate type is sub-humid tropical monsoon with extended summers from April to June, rain from July to October and winter from November to February months. The average yearly temperature ranged from 16.1 °C to 31 °C, while April and May are the hottest, whereas December and January are the coldest months with minimum temperature drops below 5 °C. The annual rainfall varies between 1350 and 1600 mm.
Layout map of the study area.
2.2 Selection, image pre-processing and classification of remote sensing data
Landsat 4, 5, 7, 8 Thematic Mapper (TM) images (1980, 1988, 1998, 2008) and a high-resolution cloud free Resourcesat 2A level-1C image of path 144 and row 44, 43 April/March 2018 was used for mapping LU/LC change classes of the upper catchment areas of Narmada river from (1980 to 2018). All Landsat images were obtained freely from The United States Geological Survey (USGS) website (http://glovis.usgs.gov/). Resourcesat 2A (2018) satellite data was procured from the NRSA National Data Center (NDC), Hyderabad. The data covers the entire study area of the Narmada river basin in the upper catchment areas of Amarkantak region and the neighboring areas. The digital analysis of the data was performed using ERDAS Imagine (Version 2013) software. The secondary data collected from SOI topomaps were analyzed using ArcGIS 10.3. A base map of the study area was prepared from Survey of India toposheets (64E/16 and 64 I/4) at a 1:50,000 scale and geometric rectification of all the imageries was performed. This map was utilized for the ground control point for exactly locating samples plots in the research site. The characteristics of selected satellite data as acquired from the USGS website is demonstrated in (Table 1). Digital image processing and supervised classification has been performed for the monitoring of six land use classes using the maximum likelihood algorithm. The delineated LU/LC classes were: Dense Mixed forest (DMF), Sal mixed forest (SMF), Open Land (OL), Agriculture (AG), Habitation/commercial building (HB) and Water Bodies (WB).
| Satellite data | Obtain Date | Row/Path | Number of bands | Swath width (Km) | Inclined Angle | Spatial resolution |
|---|---|---|---|---|---|---|
| Landsat-4 | 30/05/1980 | 144/42 | 7 | 185 | 98.2° | 30 m |
| Landsat-5 | 26/03/1988 | 144/42 | 6 | 185 | 98.2° | 30 m |
| Landsat-7 | 11/07/1998 | 144/42 | 6 | 185 | 98.2° | 30 m |
| Landsat-8 | 06/09/2008 | 144/42 | 4 | 185 | 98.2° | 15 m |
| Resourcesat-2A | 27/01/2018 | 144/43 | 3 | 740 | 98.72° | 5.8 m |
2.3 Vegetation mapping and forest health monitoring using NDVI
Many remote-sensing tools are available to support forest health information needs, and forest health specialists have been making extensive use of these tools for many years. Aerial sketch mapping is extensively used in detection of forest health in USA and Canada. Color and color infrared (CIR) aerial photos have also been used for a wide range of applications. More recently, technologies such as airborne videography, digital photography, and Earth-orbiting satellite imagery have been evaluated for their ability to provide needed information. In the current paper, the vegetation mapping (during 1980 to 2018) and forest health checkup was performed with the help of NDVI (Normalized Difference Vegetation Index). It helped in vegetation classification and forest health monitoring over the time-scale employed for the study. NDVI employs the multi-spectral remote sensing data technique to find out forest types, land use land cover pattern and change detection analysis, habitation, water bodies, open area, agricultural area with few band combinations using satellite images. Previous study suggest that natural resources are easily interpreted by computing NDVI indices like LULC changes and plant health (Gandhi, et al., 2015).
2.4 Construction of DEM (Digital Elevation Model)
The quantitative representation of terrain important for earth science studies and hydrological applications is described by Digital Elevation Model (DEM) (Mukherjee, et al., 2013). The methodology adapted for the study includes the extraction of river basin image followed by extraction of drainage network data. First the DEM images were downloaded from United States Geological Survey (USGS) website (http://glovis.usgs.gov/) following which they were successfully mosaicked. The mosaicked images were then used to delineate the river basin boundary. The images of Narmada river basin was extracted using the Arc hydro tool in ArcGIS 10.3 software.
2.5 Field survey and accuracy assessment
The reference and supplementary data were collected with the help of a field survey as a ground control point with the help of GPS and direct local field measurements were performed for ground verification. Additionally, image categorization and overall accuracy assessment of the classification were carried out. Images of 1980–2018 were evaluated through confusion matrix for the overall accuracy assessments (Congalton, 1991) and Kappa analysis (Rosenfield and Fitzpatrick-Lins, 1986). Stratified random sampling method was used for the evaluation of images in the ERDAS Imagine 2013 software. Besides field investigation, we also investigated the characteristics of understorey and groundstorey vegetation in the upper catchment area of Narmada river. During the survey, we interacted with local individuals and healers for the taxonomic identification and validation of native flora and non-timber forest produces.
2.6 LULC change detection analysis
The LULC map of 1980, 1988, 1998, 2008 and 2018 was resampled from the middle resolution of satellite data (30 m) to spatial high resolution (5.8 m) for the study of classified map and LULC change during the said period. A comparison of pixel-based study was also performed to generate changes on the pixel basis. Pairs of multispectral satellite images were compared between 1980 and 2018 using cross-tabulation to find out the information of LULC changes. Interestingly, change matrix presents significant information about vegetation changes in LULC study (Shalaby and Tateishi, 2007). All classified images change matrix were generated and analyzed using ERDAS to evaluate LULC change during 1980–2018 (Yang and Wen, 2011; Thakur et al., 2020).
3 Results and discussion
3.1 LULC and vegetation mapping
The land cover classification of the study area was done using the MLA and supervised classification, the Standard False Color Composite (SFCC) maps were employed in Fig. 3. It shows decadal changes of the study area over a period of forty years. The spatio-temporal assessment of land use categories is illustrated in (Table 2, Figs. 4 and 5). With a few exceptions, almost all the land use classes were separable in one or the other bands. Six LC and vegetation classes such as DMF, SMF, OL, AG, HB and WB were delineated. The results pertaining to the spatial extent of the different forest types and change detection (during 1980 to 2018) are presented in Table 3. It indicates a steady decline in forest and water resource areas leading to shrinkage in river basin. OL, AG and HB have increased heavily with unplanned industrialization and urbanization along the river valley. The contribution of the changes in the different vegetation classes (during 1980 to 2018) are shown in Fig. 6 as revealed by NDVI index used to employ the vegetation classification and change detection over the years.
SFCC maps of study area during 1980, 1988, 1998, 2008 and 2018.
| Class Name | Area (km2) 1980 | Area (km2) 1988 | Area (km2) 1998 | Area (km2) 2008 | Area (km2) 2018 |
|---|---|---|---|---|---|
| Dense Mixed Forest | 18.92 (53.13) | 17.79 (49.96) | 16.85 (47.32) | 14.98 (42.07) | 14.78 (41.50) |
| Sal Mixed Forest | 10.65 (29.91) | 11.21 (31.48) | 11.57 (32.49) | 9.90 (27.80) | 9.90 (27.80) |
| Open Land | 2.34 (6.57) | 2.69 (7.55) | 3.01 (8.45) | 4.98 (13.98) | 5.02 (14.10) |
| Agriculture | 0.29 (0.81) | 0.90 (2.53) | 0.98 (2.75) | 2.78 (7.81) | 2.82 (7.91) |
| Habitation/Commercial Building | 0.21 (0.59) | 0.29 (0.81) | 0.56 (1.57) | 1.54 (4.32) | 1.70 (4.77) |
| Water bodies | 3.2 (8.98) | 2.71 (7.61) | 2.64 (7.41) | 1.43 (4.01) | 1.39 (3.90) |
| Total | 35.61 | 35.61 | 35.61 | 35.61 | 35.61 |
*Parenthesis values are in percentage.
- LULC Classification of the upper catchment area of Narmada River (1980–2018).
- Information on the LULC Classified Image of upper catchment area of Narmada river during 1980, 1988, 1998, 2008 and 2018.
| Class Name | Area (km2) 1980 | Area (km2) 2018 | Difference 2018 Vs 1980 (km2) |
|---|---|---|---|
| Dense Mixed Forest | 18.92 (53.13) | 14.78 (41.50) | −4.14 (−11.63) |
| Sal Mixed Forest | 10.65 (29.91) | 9.90 (27.80) | −0.75 (−2.11) |
| Open Land | 2.34 (6.57) | 5.02 (14.10) | 2.68 (7.52) |
| Agriculture | 0.29 (0.81) | 2.82 (7.91) | 2.53 (7.10) |
| Habitation/Commercial Building | 0.21 (0.59) | 1.70 (4.77) | 1.49 (4.18) |
| Water bodies | 3.2 (8.98) | 1.39 (3.90) | −1.81 (−5.08) |
| Total | 35.61 | 35.61 |
*Parenthesis values are in percentage.
- Normalized difference vegetation index maps of the study area during 1980 to 2018.
3.1.1 LULC pattern 1980
During 1980 to 2018, the DMF was dominating category which occupied an area of 53.13% (18.92 km2), the SMF occupied 29.91% (10.65 km2) area of the land use, the OL occupied an area of 6.57% (2.34 km2), the AG was occupied by 0.81% (0.29 km2), the HB covered approximately 0.59% (0.21 km2) and the area under WB cover was 8.98% (3.20 km2) respectively. The land cover area of green vegetation (including different types of forest and agriculture) was 83.85% (29.86 km2) and rest of the land use (OL, HB, WB) were 16.15% (5.75 km2), respectively (Table 2).
3.1.2 LULC pattern 1988
In the year 1988, the contribution of the forest vegetation and agriculture crop lands was 83.97%, WB was 7.61% and other land use (i.e. OL, HB) was covered by 8.42%. The land use covers under DMF, SMF, OL, AG, HB and commercial buildings cover was 49.96% (17.79 km2), 31.48% (11.21 km2), 7.55% (2.69 km2), 2.53% (0.90 km2) and 0.81% (0.29 km2) of the total upper catchment area of Narmada river respectively (Table 2).
3.1.3 LULC pattern 1998
During the period of 1998, the land cover comprises maximum LU areas of 47.32% (16.85 km2), 32.49% (11.57 km2), 8.45% (3.01 km2), 7.41% (2.64 km2), 2.75% (0.98 km2) and 1.57% (2.64 km2) under DMF, SMF, OL, WB, AG and HB of the total land use covered in the current study area. The share of total land cover under the green vegetation (including different forest types and agriculture) was 82.56% (29.40 km2) and rest of the land use (OL, HB, WB) were 17.44% (6.21 km2) respectively (Table 2).
3.1.4 LULC pattern 2008
DMF covered an area of 14.98 km2 (42.07%), SMF 9.90 km2 (27.80%), OL 4.98 km2 (13.98%), AG 2.78 km2 (7.81%), HB 1.54 km2 (4.32%) and the WB was estimated as 1.43 km2 (4.01%) respectively in the classified map of 2008.
3.1.5 LULC pattern 2018
During recent time period, the DMF occupies an area of 14.78 km2 (41.50%), which was close to 2008 DMF value. The SMF occupies an area of 9.90 km2 (27.80%), OL 5.02 km2 (14.10%), AG 2.82 km2 (7.91%), HB 1.70 km2 (4.77%) and the WB 1.39 km2 (3.90%), respectively, in the classified map of 2018. The total study area was found to comprise of 35.61 km2 (Table 2). The forest occupies more than 85% of the total area and the remaining 15% area belongs to agricultural lands, habitation/commercial spaces and water bodies. However, the forest areas are rapidly diminishing due to habitat expansion, roads constructions and other infrastructure development.
3.2 LULC change detection (1980–2018) and accuracy assessment
The results on the pattern of LULC change detection is illustrated in Fig. 7 and represent in (Table 3 and 4). Medium resolution (Landsat 4, 5, 6 and 7 images during 1980, 1988, 1998, 2008) and high-resolution satellite images (i.e. Resourcesat 2A image for 2018) have been employed for spatial distribution of the LULC changes at upper catchment areas of Narmada river between 1980 and 2018 in Fig. 8. The changes in land use are described here for each category (DMF, SMF, AG OL, HB and WB). During study periods DMF (11.63%), SMF (2.11%) and WB (5.08%) was gradually decreased whereas, OL (7.52%), AG (7.10%) and HB (4.18%) areas were increased. Fig. 8(A–E) shows the major land use change of forest areas (DMF & SMF) 13.74% to OL, AG and HB areas. The cultivation practices and habitations have been increased respectively by 7.52%, 7.10% and 4.18% during the periods between 1980 and 2018. The expansion of OL, HB and AG account for almost 18.82% (6.70 km2) of the total LULC changes in the upper catchment area of Narmada from 1980 to 2018 as demonstrated in Table 4.
Change Detection pattern of various LC classes during assessment period 2018 Vs 1980 (km2)
| Change From | Changed to | Percentage Change | |||
|---|---|---|---|---|---|
| 1980–1988 | 1988–1998 | 1998–2008 | 2008–2018 | ||
| Dense Mixed Forest | Sal Mixed Forest | 2.8 | 0.99 | 1.5 | 0.1 |
| Open Land | 0.25 | 0.69 | 1.3 | 0 | |
| Agriculture | 0 | 0.14 | 0.98 | 0.41 | |
| Habitation/Commercial Building | 0.5 | 0 | 1.47 | 0.56 | |
| Water | 0 | 0 | 0.29 | 0.74 | |
| Sal Mixed Forest | Dense Mixed Forest | 0 | 0 | 0 | 0 |
| Open Land | 0.75 | 0 | 1.86 | 0 | |
| Agriculture | 0 | 0 | 1 | 0 | |
| Habitation/Commercial Building | 0 | 0 | 1.50 | 0 | |
| Water | 0 | 0 | 0.29 | 0 | |
| Open Land | Dense Mixed Forest | 0 | 0 | 0 | 0 |
| Sal Mixed Forest | 0 | 0 | 0.45 | 0.58 | |
| Agriculture | 0 | 0 | 0 | 0.71 | |
| Habitation/Commercial Building | 0 | 0 | 0 | 0.15 | |
| Water | 0 | 0 | 0.36 | 0.48 | |
| Agriculture | Dense Mixed Forest | 0 | 0 | 0 | 0 |
| Sal Mixed Forest | 00 | 0 | 0 | 0 | |
| Open Land | 0 | 0 | 0.11 | 0.19 | |
| Habitation/Commercial Building | 0 | 0 | 0 | 0 | |
| Water | 0 | 0 | 0 | 1.10 | |
| Habitation/Commercial Building | Dense Mixed Forest | 0 | 0 | 0 | 0 |
| Sal Mixed Forest | 0 | 0 | 0 | 0 | |
| Open Land | 0 | 0 | 1.5 | 1.98 | |
| Agriculture | 0 | 0 | 0 | 0 | |
| Water | 0 | 0 | 0.47 | 0.45 | |
| Water | Dense Mixed Forest | 0 | 0 | 0 | 0 |
| Sal Mixed Forest | 0 | 0 | 0 | 0 | |
| Open Land | 0 | 0 | 0 | 0 | |
| Agriculture | 0 | 0 | 0.25 | 0.38 | |
| Habitation/Commercial Building | 0 | 0.58 | 0.98 | 1.25 | |
- Spatial distribution of land use land cover changes at upper catchment area of Narmada River between 1980 and 2018; (A) Waterbodies, (B) Forest type, (C) Agriculture, (D) Rural areas, (E) All LULC classes. Unchanged areas are shown in white.
Current study approves the reliability and accuracy of multispectral satellite images for mapping of LULC change detection in diverse areas (Aghsaei, et al., 2020; Desta and Fetene, 2020; Aslami and Ghorbani, 2018; Aggarwal et al., 2016). The LULC studies between 1980 and 2018 in upper catchment areas of Narmada basin reveals notable changes that occurred over the four decades such as shrinkage of water bodies, conversion of dense and sal mixed forest into open forest, expansion of agricultural land to delineated forest areas, increase in commercial spaces and habitation areas etc. This has further accelerated overall ecological degradation and biodiversity loss. The problems overgrazing, construction of dams, mining and illegal settlements also pose severe pressure on existing natural resources. Many native species have been replaced by non-native species and monoculture is creating ecological imbalance. Similar demonstration on LULC and change detection in the tropical regions across the globe were reported by several researchers (El-Tantawi et al., 2019; Mishra et al., 2020; Soha and El-Raey, 2019; Olmanson and Bauer, 2016; Dutta et al., 2020; Thakur et al., 2020).
Image classification accuracy assessment of 1980, 1988, 1998, 2008 and 2018 were very constructive and overall accuracy for the LULC image was found to be 85.95% for the year 1980; 87.07% for 1988; 91.86% for 1998; 93% for 2008; and 97% for the year 2018. The accuracy assessment data is mentioned in (Table 5). Similar results on accuracy assessment were reported by Olmanson and Bauer (2016), Chetan et al. (2017), Aslami and Ghorbani (2018). This study signifies the viability of the accuracy assessment data. Furthermore, the error matrix applied over the LULC classification was based on the ancillary facts (Hossen and Negm, 2016). The present study observed significant LULC change and change in matrix also shows severe changes on green undergrowth vegetation and water bodies. The change matrix in the upper catchment area of Narmada river from 1980 to 2018 is presented in (Table 6).
| Maps | 1980 | 1988 | 1998 | 2008 | 2018 |
|---|---|---|---|---|---|
| Overall accuracy (%) | 85.95 | 87.06 | 91.86 | 93 | 97 |
| Kappa | 0.85 | 0.87 | 0.83 | 0.92 | 0.97 |
| 2018 | ||||||||
|---|---|---|---|---|---|---|---|---|
| Water bodies | Sal mixed forest | Dense mixed forest | Open Land | Agriculture | Habitation | Total | ||
| 1980 | Water bodies | 0 | 4 | 4 | 0 | 0 | 0 | 8 |
| Sal mixed forest | 1054 | 634 | 9634 | 102 | 49 | 4 | 11,477 | |
| Dense mixed forest | 0 | 59 | 59 | 523 | 47 | 47 | 735 | |
| Open Land | 1521 | 523 | 7723 | 489 | 0 | 0 | 10,256 | |
| Agriculture | 244 | 9392 | 1492 | 924 | 130 | 113 | 12,295 | |
| Habitation | 247 | 0 | 0 | 302 | 3 | 53 | 605 | |
| Total area (histogram Value) | 3066 | 10,612 | 18,912 | 2340 | 229 | 217 | 35,376 | |
| Total area (km2) | 3.2 | 10.65 | 18.92 | 2.34 | 0.29 | 0.21 | 35.61 | |
3.3 Factors responsible for LULC change/land degradation
The anthropogenic stresses were mostly responsible for changes in the LULC pattern of upper catchment area of Narmada river between 1980 and 2018. Our study results point to the increasing anthropogenic stress factors in the study area with land use changes, increase in habitation areas and commercial spaces, degradation of forests and overgrazing of grasslands, problems of siltation and pollutant influx in the river water leading to degradation of water quality as well as decreasing wetland spaces (Fig. 9). This has resulted in steady biodiversity loss in the area during the last few years. The increase in habitation areas have also resulted in overexploitation of river water for domestic as well as commercial purposes. Wastewater is added back to the system without any treatments leading to deterioration of water quality. Freshwater resources have depleted in the area steadily. Groundwater contamination is another emerging issue from the study area concerned. Therefore, the current holistic study was much needed.
Images from Study Area: Deterioration of health of Narmada River.
3.4 Spatio-temporal comparison of Digital Elevation Models (DEMs)
In order to examine the increase in spatial resolution of satellite images from Landsat 4 to Landsat 7 (30 m), middle and high resolutions images from Landsat 8 and Resourcesat 2A (15 m and 5.8 m) of Digital Elevation Models, the attraction model was used. The results of the models for 30 m, 30 m, 30 m, 15 m and 5.8 m are illustrated in Fig. 10. The association of DEMs (1980, 1988, 1998, 2008 and 2018) as ideas in the attraction models determined that DEM 5.8 m (Resourcesat 2A satellite image) has better spatial resolution than 30 m images of Landsat data series. As is represented in Fig. 8, with the intensification of the value of scale factor, the sub-pixel increases more than principal pixel and variations among the elevation models are shown well. So, it can be concluded that the increases of the scale case lead to the increase in the spatial resolution. DEM helps in creating realistic models of flow patterns and networks of the drainage basin (Pan, et al., 2019). Compared to the original DEM, the computation efficiency has been improved significantly and similar findings were reported earlier (Mokarrama and Hojati, 2017). DEM can be used for increasing spatial resolution of the study area. Previous findings are in agreements with the current study which further authenticate the importance of the study (Pan et al., 2019; Liu et al., 2019).
Digital elevation models of the upper catchment area of Narmada River during 1980–2018.
3.5 Diversity of flora found in the upper catchment of Narmada river
Structure and composition of many vegetations, having immense commercial and economic values, have been identified. The tribal communities know the proper use of medicinal plants in health care and other uses. In current study, we reported 157 plants species (herbs, tubers, grasses, climbers) comprising a total of 47 overstorey species and 86 groundstorey vegetation. There were 24 understorey vegetation reported which are traditionally utilized by local tribal communities of upper catchment area of Narmada river (Table 7). The species diversity values of AABR were lower as compared to diversity indices values reported earlier (Thakur, 2018). This study conclusively proves the extinction various plant species due to different kinds of interferences like habitation, degradation, fragmentation, constructions, encroachments, introduction of exotic species and monoculture. Several studies have identified similar ecological consequences in tropical forest ecosystems (Thakur, 2007; Thakur et al., 2014, 2017; Brar et al., 2020). Due to population pressure, forest wealth is over exploited and mixed forests are reducing. There is ever increasing developmental pressure on forest especially upper catchment areas of Narmada River. Adverse impacts of mining are degrading forests as well.
| S. No. | Common name | Scientific name | Family | Parts used | Uses |
|---|---|---|---|---|---|
| 1. | Bel | Aegle marmelos | Rutaceae | Fruit Leaf | Edible, Medicinal, Religious |
| 2. | Dhabda | Anogeissus latifolia | Combretaceae | Stem Resin | House construction, Fuel wood, Agriculture implement |
| 3. | Mohline | Bauhinia purpurea | Caesalpiniaceae | Leaf Flower | Cup and plate making, Medicinal |
| 4. | Semel | Bombax ceiba | Malvaceae | Fruit Flower | Medicine, Edible |
| 5. | Salei | Boswellia serrata Roxb. | Burseraceae | Resin | Medicine |
| 6. | Chironji | Buchanania lanzan | Anacardiaceae | Fruit seed | Edible |
| 7. | Khakra | Butea monosperma | Fabaceae | Leaf | Cup and plate making |
| 8. | Kumbhi | Careya arborea | Lecythidaceae | Bark | Fish poisoning |
| 9. | Amaltash | Casia fistula | caesalpinaceae | Fruit | Medicinal |
| 10. | Mahalimb | Cedrela toona Roxb. | Meliaceae | Stem | Furniture |
| 11. | Ghiriha | Chloroxylon swietenia | Rutaceae | Stem Bark | House Construction, Agricultural implements, Fuel wood, Fish |
| 12. | Karra | Cleistanthus collinus | Euphorbiaceae | Stem | Furniture |
| 13. | Sita phal | Custard apple | Annonaceae | Fruit Stem | Edible House Construction Agricultural implements, Fuel wood |
| 14. | Shisham | Dalbergia sisoo | Leguminosae | Stem Leaf | House Construction, Agricultural Implements, Fuel Wood, Medicinal |
| 15. | Gulmohar | Delonix regia | Leguminosae | Stem | Fuel wood |
| 16. | Dhoben | Dillenia pentagyna Roxb. | Dilleniaceae | Root | Medicinal |
| 17. | Tendu | Diospyros melanoxylon Roxb. | Ebenaceae | Fruit Leaf | Edible (When ripe) Selling |
| 18. | Bargad | Ficus benghalensis | Moraceae | Fruit | Edible |
| 19. | Peepal | Ficus religiosa | Moraceae | Whole tree Fruit Leaf | Religious Edible Fodder |
| 20. | Kekad | Garuga pinnata Roxb. | Burseraceae | Stem | Agricultural implements |
| 21. | Lendia | Lagerstroemia parviflora Roxb. | Lythraceae | Stem | Firewood Boundary wall making |
| 22. | Maida | Litsea sebifera | Lauraceae | Bark | Medicinal |
| 23. | Mahua/Guli | Madhuca indica | Sapotaceae | Flower Fruit Leaf | Edible after cooking Liquor preparation Oil Religious |
| 24. | Aam | Mangifera indica | Anacardiaaceae | Fruit Seed | Edible Edible, medicinal |
| 25. | Munga | Moringa pterygosperma Gaertn. | Moringaceae | Leaf Fruit | Edible Edible |
| 26. | Amla | Phyllanthus emblica | Euphorbiaceae | Fruit Leaf | Edible and medicinal Cultural and medicinal |
| 27. | Kanji | Pongamia pinnata | Fabaceae | Fruit | Oil extraction |
| 28. | Bija | Pterocarpus marsupium Roxb. | Faabaceae | Stem | House construction Furniture |
| 29. | Kusum | Schleichera trijuga Willd. | Sapindaceae | Fruit | Edible |
| 30. | Bhelwa | Semicarpus anacardium | Anacardiaceae | Fruit | Edible, Medicinal |
| 31. | Sarai | Shorea robusta Gaertn. | Dipterocarpaceae | Stem | House construction, Furniture, Fuel wood, Tooth brush, oil |
| 32. | Gulhar/kullu | Sterculia urens | Sterculiaceae | Resin Bark | Medicinal |
| 32. | Jamun | Syzygium cumini | Myrtaceae | Stem Fruit Leaf | Cultural, Edible, medicinal |
| 34. | Emli | Tamarindus indica | Caesalpiniaceae | Fruit | Edible, Pickle preparation, Medicinal, Selling |
| 35. | Sagaun | Tectona grandis | Lamiaceae | Stem Leaf | House construction, Furniture Furniture, Dona making |
| 36. | Arjun | Terminalia arjuna | Combretaceae | Stem | Firewood, House construction |
| 37. | Beheda | Terminalia bellirica | Combretaceae | Fruit | Medicinal (Digestive) |
| 38. | Harra | Terminalia chebula | Combretaceae | Fruit | Medicinal (Digestive) |
| 39. | Saja | Terminalia tomentosa | Combretaceae | Stem | House construction, Fuel wood, Used during marriage |
| 40. | Kala Umbar | Ficus hispida L.f. | Moraceae | Fruits | Fruits, vegetable |
| 41. | Alu Bukhara | Flacourtia indica (Burm.f.) Merr. | Salicaceae | Fruit | Fruit - raw or cooked |
| 42. | Phalsa | Grewia asiatica L | Malvaceae | Fruit | The fruit can be eaten raw, |
| 43. | Jungli sami, Khejri | Prosopis cineraria | Fabaceae | Fruit | Leaves and fruit eaten |
| 44. | Kadam | Anthocephalous kadamba | Rubiaceae | Fruits | Ripe fruits eaten |
| 45. | Kaji, Kinu | Bridelia retusa (L.) A.Juss | Phyllanthaceae | Fruit, Bark, Flowers | Ripe fruit pulp |
| 46. | Wild kajur | Phoenix sylvestris (L.) Roxb. | Arecaceae | Fruit | Ripe fruits eaten |
| 47. | Neem | Azadirachta indica A.Juss. | Meliaceae | Fruit | Pulp of ripe fruits eaten |
| 48. | Ghughch | Abrus precatorius | Fabaceae | Leaves | Mouth freshener |
| 49. | Bans | Bambusa bamboo | Poaceae | Seeds | mix into flour |
| 50. | Chakor | Cassia tora | Caesalpiniaceae | Pod and seed | Vegetable |
| 51. | Ratan jot | Jathropa curcus | Euphorbiaceae | Seed, Whole plant | Biofuel, Substitute of candle Bio-fencing |
| 52. | Lantana | Lantana camara | Verbenaceae | Ripen fruits Whole plant | Edible Bio-fencing |
| 53. | Khajuri | Phoenix sylvestris | Arecaceae | Ripen fruits | Edible. |
| 54. | Mainhar | Randia dumetorum | Rubiaceae | Leaf Root | Vegetable Medicinal |
| 55. | Arandi | Ricinis communis | Euphorbiaceae | Seed | Oil |
| 56. | Nirgundi | Vitex nigundo | Verbenaceae | Leaf | Medicinal |
| 57. | Ber | Zizipus zilopyrus | Rhamnaceae | Fruit | Edible |
| 58. | Aak | Calotropis gagentia | Musaceae | Leaf & flower | Offer to god |
| 59. | Banana | Musa paradisca | Lythraceae | Whole tree | Religious use |
| 60. | Mehandi | Lawsonia irnemis | family | Leaf | Dye |
| 61. | Sitaphal | Annona squamosa L. | Annonaceae | Fruit | Ripe fruits eaten |
| 62. | Karonda | Carissa carandas L. | Apocynaceae | Fruit | Ripe fruits eaten & pickled |
| 63. | Chota ber | Zizyphus martuiana Lam | Rhamnaceae | Fruit | Ripe fruits eaten |
| 64. | Dhavai | Woodfordia floribunda (L.). Kurz. | Lytharaceae | Flowers | Flowers are eaten as food |
| 65. | Raimuniya | Lantana camara L. | Verbenaceae | Fruit | Ripe fruits eaten |
| 66. | Makora | Ziziphus oenophylla Lam. | Rhamnaceae | Fruits | Eaten by children |
| 67. | Kathber, Baraber | Ziziphus xylopyrus (Retz.) Willd. | Rhamnaceae | Fruits | Eaten and used as drug |
| 68. | Bilangada | Flacourtia indica (Burm.f.) Merr. | Salicaceae | Fruits | Raw eaten or cooked |
| 69. | Kiraman | Grewia rothii DC. | Malvaceae | Bark | Medicinal |
| 70. | Beli | Limonia crenulata (Roxb.) Roem | Rutaceae | Fruit | Eaten |
| 71. | Munya | Meyna spinosa Roxb. | Rubiaceae | Fruit | Raw eaten |
| 72. | Gajar ghas | Parthenium hysterophorus L. | Asteraceae | Seeds | Medicinal |
| 73. | Kubbi | Ageratum conyzoide L. | Asteraceae | Leaves | Medicinal |
| 74. | Kurie | Bidens pilosa L. | Asteraceae | Leaves | Medicinal |
| 75. | Safed munga | Celosia argentea L. | Amaranthaceae | Leaves, Fruit | Medicinal & Vegetable |
| 76. | Bhrangraj | Eclipta alba) Hassk. L) | Asteraceae | Oil | Medicinal |
| 77. | Kutki | Panicum antidotale Retz. | Poaceae | Leaves | Food |
| 78. | Grass lily | Iphigenia indica.L) A. Gray ex Kunth) | Poaceae | Leaves | Fodder |
| 79. | Meethi buti | Scoparia dulcis L. | Plantaginaceae | Leaves, Seeds | Medicinal |
| 80. | Naichi bhaji | Smithia conferta Sm. | Fabaceae | Leaves | Vegetable |
| 81. | Kanghi | Blainvillea acmella (L.) Philipson | Asteraceae | Leaves | Medicinal |
| 82. | Soli | Aeschynomene americana L. | Leguminosae | Seeds/grains | Grain edible |
| 83. | Dudhali | Sopubia delphinifolia G. | Scrophulariaceae | Leaves, Seeds | Medicinal |
| 84. | Akarkara | Spilanthes paniculata Wall. ex DC. | Asteraceae | Inflorescences | Medicinal |
| 85. | Bhui amla | Phyllanthus niruri L. | Euphorbiaceae | Whole plants | Medicinal |
| 86. | Doodhi | Euphorbia heterophylla Des F. | Euphorbiaceae | Leaves | Medicinal |
| 87. | Pulpuli grass | Arthraxon hispidus (T. Makino) | Poaceae | Leaves | Fodder |
| 88. | Satawar | Asparagus racemosus Willd. | Liliaceae | Roots/Tubers | Medicinal |
| 89. | Haddi mushli | Chlorophytum borivilianum Santapau & R. R. Fern. | Asparangaceae | Tubers | Medicinal |
| 90. | Ghughunia | Crotalaria retusa L. | Leguminosae | Leaves | Food |
| 91. | Pihri chara | Mecardonia procumbens Mil Small | Scrophulariaceae | Leaves | Fodder |
| 92. | Satparni | Desmodium gangeticum L. | Fabaceae | Leaves | Medicinal |
| 93. | Kharatti | Sida acuta Burm. f. | Malvaceae | Leaves | Medicinal |
| 94. | Sitab | Ruta graveolens L. | Rutaceae | Leaves | Medicinal |
| 95. | Mameera | Thalictrum foliolosum DC. | Rananculaceae | Leaves | Medicinal |
| 96. | Bathua bhaaji | Chenopodium album L. | Chenopodiaceae | Leaves | Vegetable |
| 97. | Patthar choor | Plectranthus mollis (A) Spreng. | Lamiaceae | Roots/Tubers | Medicinal |
| 98. | Bariyari | Sida cordata (Burm. f.) Borss. Waalk. | Malvaceae | Leaves | Medicinal |
| 99. | Hirankhuri | Emilia sonchifolia (L.) DC. ex DC. | Asteraceae | Fruits | Medicinal |
| 100. | Badranj boya | Nepeta cataria L. | Lamiaceae | Seeds | Medicinal |
| 101. | Kevkand | Dioscorea bulbifera L. | Dioscoreaceae | Suckers/Tuber | Medicinal |
| 102. | Kali mushli | Curculigo orchioides Gaertn | Agavaceae | Suckers/Roots | Medicinal |
| 103. | Tinpaniya | Oxalis corniculata L. | Oxalidaceae | Leaves | Medicinal & Vegetable |
| 104. | Maskani | Evolvulus nummularius L | Convolvulaceae | Leaves | Medicinal |
| 105. | Chanchu | Corchorus fascicularis Lam. | Tiliaceae | Leaves | Food |
| 106. | Kena | Commelina diffusa Burm. f. | Commelinaceae | Roots/tubers | Medicinal |
| 107. | Kharmor | Rungia pectinata L. | Acanthaceae | Leaves/shoots | Medicinal |
| 108. | Ghueen | Fimbristylis littoralis Gaudich. | Cyperceae | Roots/leaves | Medicinal |
| 109. | Nagar motha | Cyperus gracilis R. Br. | Poaceae | Leaves, roots | Fodder and Commercial products |
| 110. | Bufalo grass | Paspalum conjugatum P. J. Bergius | Poaceae | Leaves | Fodder |
| 111. | Baiga sikiyab | Digitaria divaricatissima R. B) Hughes) | Poaceae | Leaves | Fodder |
| 112. | Jangli marua | Eleusine indica) Gaert). | Poaceae | Leaves | Fodder |
| 113. | Dokar bel | Vitis carnosa Lam Wall. | Vitaceae | Leaves/Fruits | Medicinal |
| 114. | Chhuimui | Mimosa pudica L. | Fabaceae | Leaves | Medicinal |
| 115. | Nuniya bhaji | Portulaca oleracea L. | Portulaceae | Leaves | Vegetable |
| 116. | Kanthkari | Solanum xanthocarpum Schrad. & H. Wendl. | Solanaceae | Leaves | Medicinal |
| 117. | Jungli sama | Echinochloa colona Link | Poaceae | Seeds grains | Grain edible |
| 118. | Amti | Solanum nodiflorum Jacq. | Solanaceae | Fruits | Vegetable |
| 119. | Chirchita | Achyranthes aspera L. | Amaranthaceae | Seeds, leaves | Medicinal |
| 120. | Ghooma | Leucas aspera Willd | Lamiaceae | Leaves | Vegetable |
| 121. | Kaniya kanda | Dioscorea oppositifolia L. | Dioscoreaceae | Tubers | Medicinal |
| 122. | Chench | Corchorus trilocularis L. | Tiliaceae | Leaves | Vegetable |
| 123. | Chanahur | Marsdenia tenacissima Roxb. | Asclepiadaceae | Leaves | Vegetable |
| 124. | Van rai | Blumeopsis flava D Gagnep. | Asteraceae | Seeds, Leaves | Medicinal, vegetable |
| 125. | Tikhur | Curcuma angustifolia Roxb. | Zingiberaceae | Tuber | Medicinal |
| 126. | Mandukparni | Centella asiatica L. | Apiaceae | Leaves | Medicinal |
| 127. | Ghuia | Colocasia esculenta L Schott. | Araceae | Leaves, Rhizomes | Vegetable |
| 128. | Kev kand | Costus specios J. Koen Sm. | Zingiberaceae | Tuber | Medicinal |
| 129. | Amahaldi | Curcuma amada Roxb. | Zingiberaceae | Tuber | Medicinal |
| 130. | Jungli dhania | Eryngium foetidum L. | Apiaceae | Leaves, Seeds | Vegetable |
| 131. | Bisakhpara | Boerhavia procumbens Banks ex Roxb. | Nyctaginaceae | Leaves | Vegetable |
| 132. | Badi dudhi | Euphorbia hirta L. | Euphorbiaceae | Leaves | Medicinal |
| 133. | Chhoti dudhi | Euphorbia macrophylla Pax | Euphorbiaceae | Leaves | Medicinal |
| 134. | Bara | Flemingia chappar Benth. | Fabaceae | Shoots | Lac |
| 135. | Bedarikand | Coccinia grandis Voigt | Cucurbitaceae | Climber | Edible |
| 136. | Kalihari | Gloriosa superba L. | Colchicaceae | Climbers, Flower | Medicinal |
| 137. | Kheksa | Momordica dioica Roxb | Cucurbitaceae | Climber | Vegetable |
| 138. | Karmata | Ipomoea aquatica Forssk. | Convalvulaceae | Leaves | Vegetable |
| 139. | Jungle kevanch | Mucuna pruriens L. | Papilionaceae | Seeds | Medicinal |
| 140. | Jangli pyaj | Urginea indica Roxb | Liliaceae | Tuber | Medicinal |
| 141. | Chirula | Aerva lanata L. | Amaranthaceae | Leaves | Medicinal |
| 142. | Chirinya | Peristrophe roxburghiana Roem & Schult | Acanthacea | Leaves | Medicinal |
| 143. | Garundi | Alternanthera sessilis L. | Amaranthaceae | Leaves | Medicinal |
| 144. | Jungli rye | Sisymbrium nigrum Prantl | Cruciferae | Seeds | Vegetable |
| 145. | Jangli Tulsi | Ocimum gratissimum L. | Lamiaceae | Seeds, Leaves, Inflorescences | Medicinal |
| 146. | Chirpoti | Physalis minima L. | Solanaceae | Fruits | Fruit edible |
| 147. | Sarpgandha | Rauvolfia serpentine L Benth. ex Kurz | Apocynaceae | Seeds | Medicinal |
| 148. | Sadabahar | Catharanthus roseus L G. Don | Apocynaceae | Flowers | Medicinal |
| 149. | Brahmi | Bacopa monnieri L Wettst. | Plantaginaceae | Whole plants | Medicinal |
| 150. | Tulsi | Ocimum sanctum L. | Lamiaceae | Seeds, Leaves, Inflorescences | Medicinal, Religious use |
| 151. | Chirayta | Swertia alba T. N. Ho & S. W. Liu | Gentianaceae | Whole plants | Medicinal |
| 152 | Aswagandha | Withania somnifera Dunal | Solanaceae | Suckers, leaves | Medicinal |
| 153. | Chand kal | Macaranga peltata Roxb Müll. Arg. | Euphorbiaceae | Leaves | Medicinal |
| 154. | Chaulai | Amaranthus spinosus L. | Amaranthaceae | Leaves | Vegetable |
| 155. | Tiger lily | Belamcanda chinensis L. DC. | Iridaceae | Tubers | Medicinal |
| 156. | Buch | Acorus calamus L. | Acoraceae | Rhizomes, Oil | Medicinal |
| 157. | Mandukparni | Centella asiatica L. | Plantaginaceae | Whole plants | Medicinal |
4 Conclusion
The study infers that satellite based remote sensing and GIS techniques are indeed the most reliable tools for the characterization of land use along the river basin of the concerned study area. One of the most important features of Narmada and its tributaries such as Gayatri, Savitri, Kapila, Baitarini, Arandi emerging from Amarkantak region is that all of them are fed by rain water. Ample rainfall is therefore positively correlated to the existence of these rivers which develops the core idea of sustenance in the region. These rivers originate from Maikal mountain range are under threat due to natural as well as anthropogenic causes as listed above. Holistic development guided by eco-restoration strategies is the need of the hour. Lack of monitoring and scientific approaches can bring a death blow to these rivers. The upper zone of the Narmada is very important from ecological viewpoint and needs urgent attention. The entire riparian ecosystem is dependent on good forest cover for sustainability. Vegetation mapping using NDVI reveals shrinking forest cover and degradation in forest health over the years. The anthropogenic stress factors such as increase in commercial zones, utilization of river water for irrigation purposes, agricultural run-off, industrial effluents, domestic exploitation of water resources, municipal sewage and sludge mixing with flowing water are the major factors behind the degradation of forest health which is causing negative impact on the entire environment. LULC analysis also supports these findings.
The study also reveals that Digital Elevation Model (DEM) is very useful in studying the topography within a GIS environment. Geomorphic analysis of an area is based on the systematic study of present-day landforms which can be related to their origin, nature, development, geologic changes and their relationship with other underlying structures. The technology has been effectively and economically used in the analysis and inventory of basin area development and management. The scientific investigation proves the vulnerability of Narmada and her tributaries. Relief ratio, ruggedness number and visual interpretation of the DEM of the study area indicate moderate to high relief, low run off and high infiltrations with the early mature stage of erosion.
The identification of rare, endangered and threatened species is essential for prioritization of conservation in the upper catchment area of Narmada. Application of geospatial techniques will be exploited for the expansion of spatial data sets that is quite necessary for the conservation of the forest ecosystem with sustainable approach. Further, the field surveys in selected villages help in gathering information on ground realities of socio-economic status and also traditional methods, and uses of forests produces. Finally, the study documented a total of 157 species potentially exploited and utilized by aboriginal communities of upper catchment area of Narmada in Central India. Study highlighted the unsustainable and overexploitation of resources leading to forest degradation as well. Therefore, appropriate management interventions were suggested to conserve the susceptible species as well as associated rivers of Narmada by involving the indigenous communities.
Acknowledgement
The authors are grateful to Indira Gandhi National Tribal University, Amarkantak, MP, India for continuous support and encouragement to carry out research work and the researchers and farmers involved in this study are deeply acknowledged for their support and cooperation. We are thankful to the anonymous reviewers for improving the quality of this manuscript. This research was supported by Researchers Supporting Project number (RSP-2020/249), King Saud University, Riyadh, Saudi Arabia.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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