Assessment of Vegetation Condition in Lower Narmada Basin Using Remote Sensing and GIS

Vegetation Index (NDVI) is a widely used remote sensing technique for mapping land use and land cover (LULC) categories. This method helps in identifying different vegetation types and their health status (Tiwari et al. 2017). In the discipline of remote sensing applications, scientists have created vegetation indices to qualitatively and quantitatively evaluate vegetative cover using spectral measurements. The spectral response of vegetated regions is a complicated combination of plant, soil brightness, environmental impacts, shadow, soil colour, and moisture. Furthermore, the VI is influenced by atmospheric fluctuations, both geographical and temporal, during forty vegetation indices have been established during the last two decades to improve vegetation responsiveness while minimizing the effects of the elements mentioned above (Bannari, et. al., 1995) Many river basins, investments in water infrastructure for urban, industrial, and agricultural growth are approaching or exceeding the amount of renewable water available. This over commitment of water resources is caused by disregard for environmental water requirements, incomplete hydrological knowledge, fuzzy water rights, and politically motivated projects with weak economic rationale, resulting in overbuilt river basins. The challenge for agricultural water management is to do more with less water in already stressed river basins, as well as to provide much stricter scrutiny by decision makers and civil society of new infrastructure development in relatively open river basins, in order to avoid over commitment (Molle, et. al., 2013).

STUDY AREA

India is a home to an extensive rivers network, with a total annual discharge of about 1,953 km2 (Gupta et.al. 2020). The Narmada River is the fifth-largest in India and the largest in Gujarat, spanning a total length of 1,312 km, often referred to as the lifeline of Central India, it flows through the Narmada River Basin, which extends between 72°32′ to 81°45′E longitude and 21°20′ to 23°45′N latitude, covering an area of 98,796 km² (Oza et.al, 2025). The Lower basin has major part of Gujarat state and has costal area, it covers the districts like Bharuch, Narmada, Vadodara and Choota Udaipur (Bhargav et.al. 2024). The upper hilly regions of the basin receive higher annual rainfall (1400-1650 mm), which causes floods in the downstream area even though it is a semiarid zone.  The number of large tributaries in the lower basin of the Narmada River is smaller than that in the upper and middle basin, however major tributaries of Narmada are Orsang, Heran and Karjan. (https://sites.iitgn.ac.in/cnarmada/assets/files/Report.pdf).

Fig 2.1 Study area map of Narmada basin (a) India state map covered by the basin (b) Basin map showing lower sub-basin (c) lower sub-basin with Taluka boundary.

METHODOLOGY

3.1 Vector data used

Narmada basin bounders for the present study has been download from the HydroBASINS dataset which is a section of HydroSHEDS data, available at https://www.hydrosheds.org/ (Lehner &, Grill, 2013). Further the Lower basin of the Narmada River was clipped form the main river basin using the reference maps of Water Resources Information System- WRIS (http://www.india-wris.nrsc.gov.in.) through QGIS Software.

3.2 Raster data used

Cloud free Images for 3 years 2015, 2020 and 2025 has been downloaded from USGS Earth portal (Table 3.1).

Table 3.1 Landsat data used in the study

3.3 Spectral indices calculation for vegetation status analysis

For the study various spectral indices were utilized to access the vegetation condition of Lower Narmada basin. Specifically, the like Normalized Difference Vegetation Index (NDWI), Normalized Difference Water Index (NDB), NDMI (Normalized Difference Moisture Index) and NDBI (Normalized Difference built-up Index) were computed following the methodologies outlined by Kshetri, 2018 and Malik et. al., 2020. Upon calculation, the spectral index images underwent image processing, wherein each index was classified into five classes: Very Low, Low, Moderate, High, and Very High. Subsequently an area-based statistical report was generated, facilitating the assessment of vegetation change over time. Followed by preparation of maps (Fig 3.1). For the present study software QGIS version 3.40 has been used to process both vaster and vector data.

Fig 3.1 Outline Workflow of the present study

RESULT AND DISCUSSION

4.1                                      Normalized Difference Vegetation Cover (NDVI)

The analysis of Normalized Difference Vegetation Index (NDVI) over three consecutive year 2015, 2020 and 2025 reveals distinct trends in vegetation coverage across the lower Narmada basin (Fig 4.1).

Fig 4.1 Area of NDVI for years 2015, 2020 & 2025

With an NDVI coverage of 5085.98 sq. km, Class 3 has the largest area across all three years, significantly higher than the other classes. Class 2 shows a decreasing trend in area over the years. Class 1 and Class 5 have relatively small areas compared to the other classes and show a slight increasing trend. Class 4 fluctuates in area, increasing from 2015 to 2020 and then decreasing slightly in 2025.

4.2                                          Normalized Difference Water Index (NDWI)

The analysis of Normalized Difference Water Index (NDWI) over three consecutive years 2015, 2020 and 2025 reveals distinct trends in water content across the lower Narmada basin (Fig 4.2).

Fig 4.2 Area of NDWI for years 2015, 2020 & 2025

With an NDWI coverage, Class 3 has the largest area over the course of the three years. Over time, class 2 has a declining tendency in area. Compared to the other classes, Class 1 and Class 5 have comparatively small areas and exhibit a little upward trend. Class 4 area varies, staying the same from 2015 and 2020 before somewhat growing in 2025.

4.3                                       Normalized Difference Moisture Index (NDMI)

The analysis of Normalized Difference Moisture Index (NDMI) over three consecutive years 22015, 2020 and 2025 reveals distinct trends in moisture content (Fig 4.3).

Fig 4.3 Area of NDMI for years 2015, 2020 & 2025

With an NDMI coverage over time, Class 3 exhibits a notable expansion in area, emerging as the leading class by 2025. Class 2 exhibits a notable decline in area over time. The area of Class 1 drastically shrinks, becoming insignificant by 2025. Between 2015 and 2020, Class 4 area somewhat increased, and in 2025, it slightly decreased.

4.4                                         Normalized Difference Built-up Index (NDBI)

The analysis of Normalized Difference Built-up Index (NDBI) over three consecutive years 2015, 2020 & 2025 reveals distinct trends in build-up area across the lower Narmada Basin (Fig 4.4).

Fig. 4.4 Area of NDBI for years 2015, 2020 & 2025

With an NDBI coverage of above-mentioned area, in all three years, Class 3 has the greatest area, though it shows a minor gain in 2025 after declining from 2015 to 2020. The area of Class 2 gradually grows over time. The area of Class 4 shows a notable growth between 2015 and 2020, followed by a decline in 2025. In contrast Class 1 and Class 5 have smaller spaces than the other classes. Class 1 displays a modest rise from 2015 to 2020, followed by a decline in 2025. In class 5, there is a rise from 2015 to 2020 and a fall in 2025

Table 4.1 Correlations between the vegetation indices (NDVI, NDWI, NDMI, and NDBI) over the years 2015, 2020, and 2025

4.5 Maps of the Vegetation Indices