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Altitudinal Appraisal Of Land Use Land Cover And Surface Temperature Change In The Satluj Basin, India

https://doi.org/10.24057/2071-9388-2023-2958

Abstract

The land use change has affected nearly 32% of the global landscape from 1960 to 2019. Several studies have examined the impacts of land use land cover (LULC) on the surface temperature. Still, the spatiotemporal variation of LULC and LST with altitude is a less researched area. In the current study, we assess the LULC dynamics and its relation to altitudinal LST in the Himalayan Satluj River basin in Himachal Pradesh across the altitudinal range of 332 to 6558 meters. LULC, LST, NDVI, and NDMI were derived from Landsat data for 1980-2020. The spatial pattern was analyzed using Support Vector Machine (SVM) and a mono-window algorithm. The results of LULC denote that snow covered area (SCA) have decreased by nearly 56.19% since 1980 and vegetation cover has increased. However, a decline in vegetation density is pronounced at the same time. The mean surface temperature of the Satluj basin has amplified by 6°C (0.25°C/year) from 1996 to 2020. Mostly Zone 3 and 4 are under high hilly and temperate dry regions in Lahaul Spiti and Kinnaur district of Himachal Pradesh. The most important sign is that the mean surface temperature for Zone 3 (3000m-4500m) and Zone 4 (above 4500m) was the highest increase to 6°C (0.26°C/year) and 8°C (0.31°C/year) from 1996 to 2020. The increase in LST values is attributed to land cover dynamics precisely the decline of snow cover area and the emergence of vegetation zone at higher above the 4500 altitudes. Our study facilitates regional analysis.

About the Authors

Pankaj Kumar
Department of Geography, Delhi School of Economics, University of Delhi
Russian Federation

Delhi



Swati Thakur
Department of Geography, Dyal Singh College, University of Delhi
India

New Delhi



Surajmal Junawa
Department of Geography, Delhi School of Economics, University of Delhi
Russian Federation

Delhi



Subhash Anand
Department of Geography, Delhi School of Economics, University of Delhi
Russian Federation

Delhi



References

1. Ali, S. A., Khatun, R., Ahmad, A., & Ahmad, S. N. (2019). Application of GIS-based analytic hierarchy process and frequency ratio model to flood vulnerable mapping and risk area estimation at Sundarban region, India. Modeling Earth Systems and Environment, 5(3), 1083–1102. https://doi.org/10.1007/s40808-019-00593-z

2. Artis, D. A., & Carnahan, W. H. (1982). Survey of Emissivity Variability in Thennography of Urban Areas. 329, 313–329. https://doi.org/10.1016/0034-4257(82)90043-8.

3. Bandyopadhyay, D., Mukherjee, S., Singh, G., & Coomes, D. (2023). The rapid vegetation line shift in response to glacial dynamics and climate variability in Himalaya between 2000 and 2014. Environmental Monitoring and Assessment, 195(1). https://doi.org/10.1007/s10661-022-10577-9

4. Bindajam, A. A., Mallick, J., Alqadhi, S., & Singh, C. K. (n.d.). Impacts of Vegetation and Topography on Land Surface Temperature Variability over the Semi-Arid Mountain Cities of Saudi Arabia. 1–28. Atmosphere, 11(7), 762. https://doi.org/10.3390/ATMOS11070762.

5. Chauhan, N., Upadhyay, S. K., & Singh, R. (2021). The Himalayan natural resources: Challenges and conservation for sustainable development. Article in Journal of Pharmacognosy and Phytochemistry, 10(1), 1643–1648. www.phytojournal.com

6. Chhogyel, N., Kumar, L., Bajgai, Y., & Hasan, M. K. (2020). Perception of farmers on climate change and its impacts on agriculture across various altitudinal zones of Bhutan Himalayas. International Journal of Environmental Science and Technology, 17(8), 3607–3620. https://doi.org/10.1007/s13762-020-02662-8

7. Das, S., & Angadi, D. P. (2020). Land use-land cover (LULC) transformation and its relation with land surface temperature changes: A case study of Barrackpore Subdivision, West Bengal, India. Remote Sensing Applications: Society and Environment, 19(July 2019), 100322. https://doi.org/10.1016/j.rsase.2020.100322

8. Delgado-Moreno, D., & Gao, Y. (2022). Forest Degradation Estimation Through Trend Analysis of Annual Time Series NDVI, NDMI and NDFI (2010–2020) Using Landsat Images BT - Advances in Geospatial Data Science (R. Tapia-McClung, O. Sánchez-Siordia, K. González-Zuccolotto, & H. Carlos-Martínez (eds.); pp. 149–159). Springer International Publishing.

9. Grêt-Regamey, A., Weibel, B., Bagstad, K. J., Ferrari, M., Geneletti, D., Klug, H., Schirpke, U., & Tappeiner, U. (2014). On the effects of scale for ecosystem services mapping. PLoS ONE, 9(12), 1–26. https://doi.org/10.1371/journal.pone.0112601

10. Haq, M. A., Baral, P., Yaragal, S., & Rahaman, G. (2020). Assessment of trends of land surface vegetation distribution, snow cover and temperature over entire Himachal Pradesh using MODIS datasets. Natural Resource Modeling, 33(2). https://doi.org/10.1111/nrm.12262

11. Holzman, M. E., Rivas, R., & Piccolo, M. C. (2014). Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index. International Journal of Applied Earth Observation and Geoinformation, 28(1), 181–192. https://doi.org/10.1016/j.jag.2013.12.006

12. Husain, M. A., Kumar, P., Singh, A., Raman, V. A. V, Dua, R., & Thakur, S. (2023). Snow Cover and Snowline Variation in Relation to Land Surface Temperature in Spiti Valley, Himachal Pradesh, India. International Journal of Ecology and Environmental Sciences, 49, 187–199.

13. Jin, S., & Sader, S. A. (2005). Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances. Remote Sensing of Environment, 94(3), 364–372. https://doi.org/10.1016/j.rse.2004.10.012

14. John, A., Cannistra, A. F., Yang, K., Tan, A., Shean, D., Hille Ris Lambers, J., & Cristea, N. (2022). High-Resolution Snow-Covered Area Mapping in Forested Mountain Ecosystems Using PlanetScope Imagery. Remote Sensing, 14(14), 1–24. https://doi.org/10.3390/rs14143409

15. Khan, A., Haque, S. M., & Biswas, B. (2023). Altitudinal Shifting of Apple Orchards with Adaption of Changing Climate in the Alpine Himalaya. Journal of the Indian Society of Remote Sensing, 51(5), 1135–1155. https://doi.org/10.1007/s12524-023-01678-0

16. Kumar, P., Husain, A., Singh, R. B., & Kumar, M. (2018). Impact of land cover change on land surface temperature: A case study of Spiti Valley. Journal of Mountain Science, 15(8), 1658–1670. https://doi.org/10.1007/s11629-018-4902-9

17. Li, Z., Jia, L., & Lu, J. (2015). On uncertainties of the Priestley-Taylor/LST-Fc feature space method to estimate evapotranspiration: Case study in an arid/semiarid region in northwest China. Remote Sensing, 7(1), 447–466. https://doi.org/10.3390/rs70100447

18. Lutz, A. F., Immerzeel, W. W., Gobiet, A., Pellicciotti, F., & Bierkens, M. F. P. (2013). Comparison of climate change signals in CMIP3 and CMIP5

19. multi-model ensembles and implications for Central Asian glaciers. Hydrol. Earth Syst. Sci., 17(9), 3661–3677. https://doi.org/10.5194/hess-17-3661-2013

20. MARKHAM, B. L., & BARKER, J. L. (1985). Spectral characterization of the LANDSAT Thematic Mapper sensors. International Journal of Remote Sensing, 6(5), 697–716. https://doi.org/10.1080/01431168508948492

21. Maurya, R., Negi, V. S., & Pandey, B. W. (2021). Spatio-temporal analysis of land use/land cover change through overlay technique in Kinnaur district of Himachal pradesh, Western Himalaya. Sustainability, Agri, Food and Environmental Research, 9(1). https://doi.org/10.7770/safer-v0n0-art2161

22. Monserud, R. A., & Leemans, R. (1992). Comparing global vegetation maps with the Kappa statistic. Ecological Modelling, 62(4), 275–293. https://doi.org/10.1016/0304-3800(92)90003-W

23. Mountrakis, G., Im, J., & Ogole, C. (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3), 247–259. https://doi.org/10.1016/j.isprsjprs.2010.11.001

24. Njoku, E. A., & Tenenbaum, D. E. (2022). Remote Sensing Applications : Society and Environment Quantitative assessment of the relationship between land use / land cover ( LULC ), topographic elevation and land surface temperature ( LST ) in Ilorin , Nigeria. Remote Sensing Applications: Society and Environment, 27, 100780. https://doi.org/10.1016/j.rsase.2022.100780

25. Pal, M., & Mather, P. M. (2006). Some issues in the classification of DAIS hyperspectral data. International Journal of Remote Sensing, 27(14), 2895–2916. https://doi.org/10.1080/01431160500185227

26. Pal, S., & Ziaul, S. (2017). Detection of land use and land cover change and land surface temperature in English Bazar urban centre. Egyptian Journal of Remote Sensing and Space Science, 20(1), 125–145. https://doi.org/10.1016/j.ejrs.2016.11.003

27. Pang, G., Chen, D., Wang, X., & Lai, H. W. (2022). Spatiotemporal variations of land surface albedo and associated influencing factors on the Tibetan Plateau. Science of the Total Environment, 804, 150100. https://doi.org/10.1016/j.scitotenv.2021.150100

28. Rani, S., & Mal, S. (2022). Trends in land surface temperature and its drivers over the High Mountain Asia. Egyptian Journal of Remote Sensing and Space Science, 25(3), 717–729. https://doi.org/10.1016/j.ejrs.2022.04.005

29. Roy, P. S., Ramachandran, R. M., Paul, O., Thakur, P. K., Ravan, S., Behera, M. D., Sarangi, C., & Kanawade, V. P. (2022). Anthropogenic Land Use and Land Cover Changes—A Review on Its Environmental Consequences and Climate Change. Journal of the Indian Society of Remote Sensing, 50(8), 1615–1640. https://doi.org/10.1007/s12524-022-01569-w

30. Satti, Z., Naveed, M., Shafeeque, M., Ali, S., Abdullaev, F., Ashraf, T. M., Irshad, M., & Li, L. (2023). Effects of climate change on vegetation and snow cover area in Gilgit Baltistan using MODIS data. Environmental Science and Pollution Research, 30(7), 19149–19166. https://doi.org/10.1007/s11356-022-23445-3

31. Shahfahad, Kumari, B., Tayyab, M., Ahmed, I. A., Baig, M. R. I., Khan, M. F., & Rahman, A. (2020). Longitudinal study of land surface temperature (LST) using mono- and split-window algorithms and its relationship with NDVI and NDBI over selected metro cities of India. Arabian Journal of Geosciences, 13(19). https://doi.org/10.1007/s12517-020-06068-1

32. Singh, P., & Kumar, N. (1997). Impact assessment of climate change on the hydrological response of a snow and glacier melt runoff dominated Himalayan river. Journal of Hydrology, 193(1–4), 316–350. https://doi.org/10.1016/S0022-1694(96)03142-3

33. Snyder, W. C., & Wan, Z. (1998). BRDF models to predict spectral reflectance and emissivity in the thermal infrared. IEEE Transactions on Geoscience and Remote Sensing, 36(1), 214–225. https://doi.org/10.1109/36.655331

34. Swain, S., Mishra, S. K., Pandey, A., & Kalura, P. (2022). Inclusion of groundwater and socio-economic factors for assessing comprehensive drought vulnerability over Narmada River Basin, India: A geospatial approach. Applied Water Science, 12(2), 1–16. https://doi.org/10.1007/s13201-021-01529-8

35. Taripanah, F., & Ranjbar, A. (2021). Quantitative analysis of spatial distribution of land surface temperature (LST) in relation Ecohydrological, terrain and socio- economic factors based on Landsat data in mountainous area. Advances in Space Research, 68(9), 3622–3640. https://doi.org/10.1016/j.asr.2021.07.008

36. TOWNSHEND, J. R. G., & JUSTICE, C. O. (1986). Analysis of the dynamics of African vegetation using the normalized difference vegetation index. International Journal of Remote Sensing, 7(11), 1435–1445. https://doi.org/10.1080/01431168608948946

37. Upadhayaya, P. K. (2015). Sustainability Threats to Mountain Tourism with Tourist Mechanized Mobility Induced Global Warming: A Case Study of Nepal. Journal of Tourism & Hospitality, 04(02). https://doi.org/10.4172/2167-0269.1000148

38. Vannier, C., Lasseur, R., Crouzat, E., Byczek, C., Lafond, V., Cordonnier, T., Longaretti, P. Y., & Lavorel, S. (2019). Mapping ecosystem services bundles in a heterogeneous mountain region. Ecosystems and People, 15(1), 74–88. https://doi.org/10.1080/26395916.2019.1570971

39. Voogt, J. A., & Oke, T. R. (2003). Thermal remote sensing of urban climates. Remote Sensing of Environment, 86(3), 370–384. https://doi.org/10.1016/S0034-4257(03)00079-8

40. Wen, X. (2020). Temporal and spatial relationships between soil erosion and ecological restoration in semi-arid regions: a case study in northern Shaanxi, China. GIScience and Remote Sensing, 57(4), 572–590. https://doi.org/10.1080/15481603.2020.1751406

41. Worku, G., Teferi, E., & Bantider, A. (2021). Assessing the effects of vegetation change on urban land surface temperature using remote sensing data: The case of Addis Ababa city, Ethiopia. Remote Sensing Applications: Society and Environment, 22(April), 100520. https://doi.org/10.1016/j.rsase.2021.100520

42. Xystrakis, F., Psarras, T., & Koutsias, N. (2017). A process-based land use/land cover change assessment on a mountainous area of Greece during 1945–2009: Signs of socio-economic drivers. Science of the Total Environment, 587–588, 360–370. https://doi.org/10.1016/j.scitotenv.2017.02.161

43. Young, K. R. (2014). Ecology of land cover change in glaciated tropical mountains. Revista Peruana de Biología, 21(3), 259–270.

44. Zhang, F., Zeng, B., Yang, T., Zheng, Y., & Cao, Y. (2022). A Multi-Perspective Assessment Method with a Dynamic Benchmark for Human Activity Impacts on Alpine Ecosystem under Climate Change. Remote Sensing, 14(1). https://doi.org/10.3390/rs14010208

45. Zhang, H., Zhan, C., Xia, J., & Yeh, P. J. F. (2022). Responses of vegetation to changes in terrestrial water storage and temperature in global mountainous regions. Science of the Total Environment, 851(July), 158416. https://doi.org/10.1016/j.scitotenv.2022.158416

46. Zhang, J., Wang, Y., & Li, Y. (2006). A C++ program for retrieving land surface temperature from the data of Landsat TM/ETM+ band6. Computers and Geosciences, 32(10), 1796–1805. https://doi.org/10.1016/j.cageo.2006.05.001

47. Zhang, R., Tang, X., You, S., Duan, K., Xiang, H., & Luo, H. (2020). A novel feature-level fusion framework using optical and SAR remote sensing images for land use/land cover (LULC) classification in cloudy mountainous area. Applied Sciences (Switzerland), 10(8), 1–24. https:// doi.org/10.3390/APP10082928

48. Zhongming, Z., Linong, L., Xiaona, Y., Wangqiang, Z., & Wei, L. (2021). Climate change leads to 18.52% decrease in snow cover in Himachal: Study.


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For citations:


Kumar P., Thakur S., Junawa S., Anand S. Altitudinal Appraisal Of Land Use Land Cover And Surface Temperature Change In The Satluj Basin, India. GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY. 2023;16(4):26-38. https://doi.org/10.24057/2071-9388-2023-2958

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ISSN 2071-9388 (Print)
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