Quantifying Land Degradation in Upper Catchment of Narmada River in Central India: Evaluation Study Utilizing Landsat Imagery
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Location
2.2. Data Collection
2.3. Methodologies Adopted
2.3.1. LULC Classification
2.3.2. Change Detection Analysis
2.3.3. Conversion of a Digital Number to Visibility LST Using Landsat 5 Thematic Mapper
2.3.4. Radiance to Satellite Temperature Conversion
2.3.5. Obtaining the Ground Surface’s Emissivity
2.3.6. Normalized Difference Vegetation Index (NDVI)
2.3.7. NDVI Estimate for Measuring Land Degradation
2.3.8. Drivers of Land Degradation and Land Degradation Vulnerability
3. Results and Discussion
3.1. Evaluation of the LULC Classification’s Accuracy
3.2. The LULC’s Spatial and Temporal Pattern
3.3. Land Surface Temperature Variation between 2000 and 2022
3.4. Evaluation of the LST Estimate
3.5. Spatial Trend between LST, NDVI, and LULC Relationship
3.6. Land Degradation Vulnerability Index (LDVI) Analysis
3.7. Future Perspective: An Integrated Narmada River Basin Restoration Approach for Degraded Land Reclamation
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Satellite | Source | Date Acquired | Band | Spectral Range (Wavelength µm) | Spatial Resolution (m) |
---|---|---|---|---|---|
Landsat 8 | USGS | 15 August 2022 | 1 | 0.43–0.45 | 30 |
2 | 0.45–0.51 | 30 | |||
3 | 0.64–0.67 | 30 | |||
4 | 0.53–0.59 | 30 | |||
5 | 0.85–0.88 | 30 | |||
6 | 1.57–1.65 | 30 | |||
7 | 2.11–2.29 | 30 | |||
8 | 1.36–1.38 | 30 | |||
9 | 0.50–0.68 | 30 | |||
10 (TRIS 1) | 10.60–11.19 | 100 Resampled to 30 | |||
11 (TRIS 1) | 11.50–12.51 | 100 resampled to 30 | |||
Landsat 7 | USGS | 15 November 2010 | 1 | 0.45–0.52 | 30 |
2 | 0.52–0.60 | 30 | |||
3 | 0.63–0.69 | 30 | |||
4 | 0.77–0.90 | 30 | |||
5 | 1.55–1.75 | 30 | |||
6 | 10.40–12.50 | 60 | |||
7 | 2.08–2.35 | 30 | |||
8 | 0.52–0.90 | 15 | |||
Landsat 5 | USGS | 15 December 2000 | 1 | 0.45–0.52 | 30 |
2 | 0.52–0.60 | 30 | |||
3 | 0.63–0.69 | 30 | |||
4 | 0.76–0.90 | 30 | |||
5 | 1.55–1.75 | 30 |
User Accuracy (%) | Producer Accuracy (%) | Classification Accuracy | Kappa Statistics | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Year | Water Body | Built-Up Land | Agriculture | Forest | Fallow Land | Water Body | Built-Up Land | Agriculture | Forest | Fallow Land | ||
2000 | 100 | 85 | 85 | 85 | 80 | 100 | 100 | 73.44 | 71.44 | 100 | 90.00% | 0.81 |
2010 | 100 | 75 | 100 | 100 | 85 | 100 | 100 | 100 | 100 | 66.68 | 87.67% | 0.75 |
2022 | 100 | 85 | 100 | 100 | 80 | 100 | 100 | 87.34 | 83.34 | 100 | 91% | 0.82 |
S. No. | Classes | 2000 | 2010 | 2022 | Change Detection 2000–2022 (km)2 Increase (+) or Decrease (−) | |||
---|---|---|---|---|---|---|---|---|
Area (km)2 | Area (%) | Area (km)2 | Area (%) | Area (km)2 | Area (%) | |||
1 | Water Body | 213.71 | 2.3 | 289.71 | 3.06 | 313.13 | 3.31 | +99.37 |
2 | Built-Up Land | 81.85 | 0.9 | 124.53 | 1.31 | 242.69 | 2.57 | +160.79 |
3 | Agriculture | 5607.62 | 59.3 | 5693.87 | 60.18 | 5651.20 | 59.73 | +43.62 |
4 | Forest | 2838.45 | 30.0 | 2698.54 | 28.52 | 2690.09 | 28.43 | −147.55 |
5 | Fallow Land | 719.47 | 7.6 | 654.43 | 6.91 | 563.98 | 5.96 | −155.68 |
9461.09 | 100.0 | 9461.09 | 100 | 9461.09 | 100.00 |
2022 | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
LULC Change | Water Body | Built-Up Land | Agriculture | Forest | Fallow Land | ||||||
Area in | Area in | Area in | Area in | Area in | Area in | Area in | Area in | Area in | Area in | ||
2000 | (sq. km) | (%) | (sq. km) | (%) | (sq. km) | (%) | (sq. km) | (%) | (sq. km) | (%) | |
Water Body | 0 | 0 | 18.32 | 11.39 | 9.28 | 21.27 | 19.97 | 13.72 | 14.78 | 9.49 | |
Built-Up Land | 15.45 | 15.55 | 0 | 0 | 12.1 | 27.74 | 46.89 | 32.22 | 39.08 | 25.10 | |
Agriculture | 34.21 | 34.43 | 47.85 | 29.76 | 0 | 0 | 37.3 | 25.63 | 41.83 | 26.87 | |
Forest | 20.41 | 20.54 | 55.7 | 34.64 | 15.04 | 34.48 | 0 | 0 | 59.99 | 38.53 | |
Fallow Land | 29.3 | 29.49 | 38.92 | 24.21 | 7.2 | 16.51 | 41.39 | 28.44 | 0 | 0 | |
Total Area | 99.37 | 100 | 160.79 | 100 | 43.62 | 100 | 145.55 | 100 | 155.68 | 100 |
Year | 2000 | 2010 | 2022 | |||
---|---|---|---|---|---|---|
Source of estimated/recorded LST | Maximum | Minimum | Maximum | Minimum | Maximum | Minimum |
Remotely sensed estimated LST (°C) | 25.32 | 17.42 | 33 | 18.50 | 38.13 | 26.12 |
IMD-recorded LST (°C) | 28.2 | 21.01 | 29.61 | 22.22 | 34.01 | 24.91 |
Deviation (°C) | 2.97 | 3.08 | −3.88 | 3.7 | −4.75 | −1.76 |
Deviation (%) | 10.30 | 16.92 | −13.15 | 16.67 | −14.08 | −7.02 |
LDVI Classes | Area (km2) 2022 | Percentage |
---|---|---|
Very Low Vulnerability | 2289.25 | 24.20 |
Low Vulnerability | 1319.65 | 13.95 |
Moderate Vulnerability | 1213.22 | 12.82 |
High Vulnerability | 1134.1 | 11.99 |
Very High Vulnerability | 2949.05 | 31.17 |
Built up | 242.69 | 2.57 |
Water Bodies/Drainage | 313.13 | 3.31 |
Total | 9461.09 | 100.00 |
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Patel, D.K.; Thakur, T.K.; Thakur, A.; Pandey, A.; Kumar, A.; Kumar, R.; Husain, F.M. Quantifying Land Degradation in Upper Catchment of Narmada River in Central India: Evaluation Study Utilizing Landsat Imagery. Water 2024, 16, 2440. https://doi.org/10.3390/w16172440
Patel DK, Thakur TK, Thakur A, Pandey A, Kumar A, Kumar R, Husain FM. Quantifying Land Degradation in Upper Catchment of Narmada River in Central India: Evaluation Study Utilizing Landsat Imagery. Water. 2024; 16(17):2440. https://doi.org/10.3390/w16172440
Chicago/Turabian StylePatel, Digvesh Kumar, Tarun Kumar Thakur, Anita Thakur, Amrisha Pandey, Amit Kumar, Rupesh Kumar, and Fohad Mabood Husain. 2024. "Quantifying Land Degradation in Upper Catchment of Narmada River in Central India: Evaluation Study Utilizing Landsat Imagery" Water 16, no. 17: 2440. https://doi.org/10.3390/w16172440