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Original article
03 2020
:33;
101322
doi:
10.1016/j.jksus.2020.101322

Assessment of decadal land use dynamics of upper catchment area of Narmada River, the lifeline of Central India

, , , , , , , ,
a Department of Environmental Science, Indira Gandhi National Tribal University, Amarkantak, MP 484887, India
b Department of Environmental Science, Sant Gahira Guru University, Sarguja, Ambikapur, CG 497001, India
c Department of Botany, Indira Gandhi National Tribal University, Amarkantak, MP 484887, India
d College of Forestry, VCSG Uttarakhand University of Horticulture and Forestry, Ranichauri-249199, Tehri Garhwal, Uttarakhand, India
e Department of Geology & Geophysics, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
f Department of Botany, Hindu College Moradabad (Mahatma Jyotiba Phule Rohilkhand University Bareilly), 244001, India

⁎Corresponding author. tarun.thakur@igntu.ac.in (Tarun Kumar Thakur)

Received: , Accepted: ,
© The Author(s) 2020
Disclaimer:
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

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

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.
Fig. 1
Methodology workflow and data analysis.

2.1

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.
Fig. 2
Layout map of the study area.

2.2

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).

Table 1 Characteristics of the selected Satellite data.
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

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

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

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

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

3 Results and discussion

3.1

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.
Fig. 3
SFCC maps of study area during 1980, 1988, 1998, 2008 and 2018.
Table 2 Land use land cover in upper catchment area of Narmada river between 1980 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).
Fig. 4
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.
Fig. 5
Information on the LULC Classified Image of upper catchment area of Narmada river during 1980, 1988, 1998, 2008 and 2018.
Table 3 LULC change detection analysis of upper catchment area of Narmada river during 1980 to 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.
Fig. 6
Normalized difference vegetation index maps of the study area during 1980 to 2018.

3.1.1

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

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

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

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

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

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)
Fig. 7
Change Detection pattern of various LC classes during assessment period 2018 Vs 1980 (km2)
Table 4 Pattern of LULC changes in the upper catchment area of Narmada river between 1980 and 2018.
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.
Fig. 8
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).

Table 5 Summary of Accuracy assessment
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
Table 6 Change matrices calculation of upper catchment area of Narmada river during 1980–2018
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

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.
Fig. 9
Images from Study Area: Deterioration of health of Narmada River.

3.4

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.
Fig. 10
Digital elevation models of the upper catchment area of Narmada River during 1980–2018.

3.5

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.

Table 7 List of the overstorey, understorey and groundstorey vegetation of upper catchment area of Narmada River with their uses
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

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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