Spatial variations of LST and NDVI in Muzaffarpur district, Bihar using Google earth engine (GEE) during 1990-2020

Authors

  • BHARTENDU SAJAN Centre for Climate Change and Water Research, Suresh Gyan Vihar University, Jaipur-302017, Rajasthan, India
  • SHRUTI KANGA Department of Geography, School of Environment and Earth Sciences, Central University of Punjab, VPO-Ghudda, Bathinda 151401, Punjab, India
  • SURAJ KUMAR SINGH Centre for Sustainable Development, Suresh Gyan Vihar University, Jaipur 302017, Rajasthan, India
  • VARUN NARAYAN MISHRA Amity Institute of Geoinformatics & Remote Sensing (AIGIRS), Amity University, Sector 125, Noida 201313, Gautam Buddha Nagar, India
  • BOJAN DURIN Department of Civil Engineering, University North, Varaždin 42000, Croatia

DOI:

https://doi.org/10.54386/jam.v25i2.2155

Keywords:

Land Use/Land Cover, Land Surface Temperature, NDVI, Correlation

Abstract

The aim of this study is to analyze land cover changes and their effects on land surface temperature (LST) and normalized difference vegetation index (NDVI) in Muzaffarpur district, Bihar, India. The research utilized Landsat 5 and 8 satellite images taken every five years from 1990 to 2020 to classify seven land cover types, namely built-up areas, wetlands, fallow lands, croplands, vegetation, and water bodies, using the Artificial Neural Network technique in ENVI 5.1. The resulting land cover maps reveal a significant decrease in cropland area during the studied period, while fallow land area decreased from 48.06% to 35.79%. Analysis of LST and NDVI data showed a strong negative correlation (R2 < -0.0057) for all years, except for a weak positive correlation (R2 > 0.006). NDVI values were highest in agricultural lands with the lowest LST values, while fallow land areas showed the opposite trend. The study suggests that vegetation and fallow land are crucial determinants of the spatial and temporal variations in NDVI and LST, relative to urban and water cover categories.

 

References

Alemu, M. M. (2019). Analysis of Spatio-temporal Land Surface Temperature and Normalized Difference Vegetation Index Changes in the Andassa Watershed, Blue Nile Basin, Ethiopia. J. Res. Eco., 10(1): 77-85. doi:10.5814/j.issn.1674-764x.2019.01.010

Bhagia, N, Oza, M.P., Rajak, D.R., and Dadhwal, V.K. (2005). Wheat Yield Forecast Models Using Temperature Based Simple and Weighted Indices for Punjab and Western Uttar Pradesh. J. Agrometeorol., 7 (1): 115–19. https://doi.org/10.54386/jam.v7i1.823

Chaudhari, K.N., Tripathy, R., and Patel, N.K. (2010). Spatial Wheat Yield Prediction Using Crop Simulation Model, GIS, Remote Sensing and Ground Observed Data. J. Agrometeorol., 12 (2): 174–80. https://doi.org/10.54386/jam.v12i2.1300

Chen, X. L., Zhao, H. M., Li, P. X., and Yin, Z. Y. (2006). Remote sensing image-based analysis of the relationship between. Remote Sens. Environ., 104(2006), 133-146. DOI: 10.1016/j.rse.2005.11.016

Dadhwal, V. K., and Yamini Bhat. (2023). Revisiting statistical spectral-agrometeorological wheat yield models for Punjab using MODIS EVI and NCMRWF re-analysis temperature data. J. Agrometeorol., 25(1):10–17. https://doi.org/10.54386/jam.v25i1.2067

Dash, P., Göttsche, F.-M., Olesen, F.-S., and Fischer, H. (2002). Land surface temperature and emissivity estimation from passive sensor data: theory and practice; current trends. International J. Remote Sens., 23(13): 2563. doi: 10.1080/01431160110115041

Gorgani, A. S., Panahi, M., and Rezaie, F. (2013). The Relationship between NDVI and LST in the urban area of Mashhad, Iran. International Conference on Civil Engineering Architecture & Urban Sustain. Devel., 1-7.

Kumar, D.A., Neelima, T., Srikanth, P., Devi, M.U., Suresh, K. and Murthy, C. (2022). Maize yield prediction using NDVI derived from Sentinal 2 data in Siddipet district of Telangana state. J. Agrometeorol., 24 (2): 165-168. http://doi.org/10.54386/jam.v24i2.1635

Lambin, E., Turner, B., Geist, H. J., Agbola, S., Angelsen, A., Bruce, J., and Xu, J. (2001). The causes of land-use and land-cover change: moving beyond the myths. Global Environ. Change, 11: 261-269.

Lin, B. B., Philpott, S. M., and Jha, S. (2015). The future of urban agriculture and biodiversity-ecosystem services: Challenges and next steps. Gfo Ecol. Soc. Germany, Austria and Switzerland, 16(3): 189-201.

Masek, J.G., Vermote, E.F., Saleous, N., Wolfe, R., Hall, F.G., Huemmrich, F., Gao, F., Kutler, J., and Lim, T.K. (2017). LEDAPS Landsat Calibration, Reflectance, Atmospheric Correction Preprocessing Code. Orn. Dac. NASA, 2. http://dx.doi.org/10.3334/ORNLDAAC/1146

Rakib, A. A., Akter, S. K., Rahman, M. N., Arpi, S., and Kafy, A. A. (2020). Analyzing the Pattern of Land Use Land Cover Change and its Impact on Land Surface Temperature: A Remote Sensing Approachin Mymensingh, Bangladesh. (B. University of Dhaka, Ed.) Dhaka University Research Society, 1-10.

Richards, D. R., and Belcher, R. N. (2019). Global Changes in Urban Vegetation Cover. Remote Sen., 12(1): 23. https://doi.org/10.3390/rs12010023

Zhu, Z., and Curtis E, W. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sens. Environ., 118:83-94. https://doi.org/10.1016/j.rse.2011.10.028

Zhu, Z., Wang S., and Curtis, W. E. (2015). Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsats 4-7, 8, and Sentinel 2 images. Remote Sens. Environ., 159: 269-277. https://doi.org/10.1016/j.rse.2014.12.014

Downloads

Published

25-05-2023

How to Cite

BHARTENDU SAJAN, SHRUTI KANGA, SURAJ KUMAR SINGH, VARUN NARAYAN MISHRA, & BOJAN DURIN. (2023). Spatial variations of LST and NDVI in Muzaffarpur district, Bihar using Google earth engine (GEE) during 1990-2020. Journal of Agrometeorology, 25(2), 262–267. https://doi.org/10.54386/jam.v25i2.2155

Issue

Vol. 25 No. 2 (2023): June (Silver Jubilee Publication)

Section

Research Paper

License

Copyright (c) 2023 BHARTENDU SAJAN, SHRUTI KANGA, SURAJ KUMAR SINGH, VARUN NARAYAN MISHRA, BOJAN DURIN

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

This is a human-readable summary of (and not a substitute for) the license. Disclaimer.

You are free to:

Share — copy and redistribute the material in any medium or format

Adapt — remix, transform, and build upon the material

The licensor cannot revoke these freedoms as long as you follow the license terms.

    Under the following terms:

    Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

    NonCommercial — You may not use the material for commercial purposes.

    ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.

    No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

    Notices:

    You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.

    No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.