IJCOPE Journal

UGC Logo DOI / ISO Logo

International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
Journal Information
ISSN: 3108-1754 (Online)
Crossref DOI: Available
ISO Certification: 9001:2015
Publication Fee: 599/- INR
Compliance: UGC Journal Norms
License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 6

Published on: June 2026

SPATIO TEMPORAL DYNAMICS OF SUMMER LAND SURFACE TEMPERATURE, VEGETATION COOLING EFFECT, AND URBAN HEAT ISLAND CHARACTERISTICS IN JAMMU PROVINCE, WESTERN HIMALAYA (2000–2025)

Rakesh verma Meenu Sharma

Department of Geology, Cluster University of Jammu, Jammu 180001, India

Article Status

Plagiarism Passed Peer Reviewed Open Access

Available Documents

Abstract

Land surface temperature (LST) is a key indicator of environmental change and urban thermal stress, particularly in rapidly transforming mountain landscapes. This study investigates the spatio‑temporal dynamics of summer LST, vegetation greenness, vegetation cooling effects, and urban heat island (UHI) characteristics across Jammu Province, Western Himalaya, from 2000 to 2025 using MODIS‑derived LST (MOD11A2) and NDVI (MOD13Q1) products processed in Google Earth Engine (GEE; Wan, 2014; Didan, 2015; Gorelick et al., 2017). Annual summer (May–July) composites were generated, and long‑term thermal trends were quantified using Sen’s slope estimator, while NDVI–LST regressions quantified vegetation cooling effects and NDVI–LST coupling (Kendall, 1975; Sen, 1968). LST values were converted from scaled MOD11A2 digital numbers to degrees Celsius using a 0.02 scale factor and a Kelvin–Celsius offset, and NDVI was rescaled from MOD13Q1 using a 0.0001 scale factor (Wan, 2014; Didan, 2015; LP DAAC, 2021). Results show a gradual decline in mean summer LST from approximately 25.1 °C in 2000 to 23.7 °C in 2025 (−1.4 °C, ≈−0.056 °C yr⁻¹), while mean NDVI increased from 0.447 to 0.516 (≈15.4% increase in greenness). Histogram analysis indicates that most of Jammu Province experiences summer LST between 20 °C and 32 °C, with localized thermal hotspots (>30 °C) concentrated in low‑elevation urban and industrial zones. Sen’s slope analysis reveals that most pixels exhibit stable to cooling trends, with slopes predominantly between −0.10 and 0.00 °C yr⁻¹. A strong negative NDVI–LST relationship confirms the substantial cooling influence of vegetation, indicating that increased vegetation cover has mitigated regional thermal stress. The findings highlight the importance of afforestation, urban greening, and ecosystem‑based adaptation in mitigating UHI intensity and enhancing climate resilience in the Western Himalayan region.

Keywords: land surface temperature; NDVI; urban heat island; vegetation cooling effect; Google Earth Engine; MODIS; Jammu Province; Western Himalaya, summer land surface temperature.

How to Cite this Paper

verma, R. & Sharma, M. (2026). Spatio Temporal Dynamics of Summer Land Surface Temperature, Vegetation Cooling Effect, and Urban Heat Island Characteristics in Jammu Province, Western Himalaya (2000–2025). International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(6). https://doi.org/10.55041/ijcope.v2i6.321

verma, Rakesh, and Meenu Sharma. "Spatio Temporal Dynamics of Summer Land Surface Temperature, Vegetation Cooling Effect, and Urban Heat Island Characteristics in Jammu Province, Western Himalaya (2000–2025)." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 6, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i6.321.

verma, Rakesh, and Meenu Sharma. "Spatio Temporal Dynamics of Summer Land Surface Temperature, Vegetation Cooling Effect, and Urban Heat Island Characteristics in Jammu Province, Western Himalaya (2000–2025)." International Journal of Creative and Open Research in Engineering and Management 02, no. 6 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i6.321.

Search & Index

References


  1. Bonan, G. B. (2008). Forests and climate change: Forcings, feedbacks, and the climate benefits of forests. Science, 320(5882), 1444–1449.

  2. Bowler, D. E., Buyung‑Ali, L., Knight, T. M., & Pullin, A. S. (2010). Urban greening to cool towns and cities: A systematic review of the empirical evidence. Landscape and Urban Planning, 97(3), 147–155.

  3. Chakraborty, T., & Lee, X. (2019). A simplified urban‑extent algorithm to characterize surface urban heat islands on a global scale and examine vegetation control on their spatiotemporal variability. International Journal of Applied Earth Observation and Geoinformation, 74, 269–280.

  4. Didan, K. (2015). MOD13Q1 MODIS/Terra Vegetation Indices 16‑Day L3 Global 250m SIN Grid V006 [Data set]. NASA EOSDIS Land Processes DAAC.

  5. Farr, T. G., et al. (2007). The Shuttle Radar Topography Mission (SRTM). Reviews of Geophysics, 45(2), RG2004.

  6. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary‑scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27.

  7. Hallett, R. A., et al. (2022). Climate change and urban forests. In Climate change and forests of the future (pp. XX–XX). USDA Forest Service, Northern Research Station.

  8. Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1–2), 195–213.

  9. Imhoff, M. L., Zhang, P., Wolfe, R. E., & Bounoua, L. (2010). Remote sensing of the urban heat island effect across biomes in the continental USA. Remote Sensing of Environment, 114(3), 504–513.

  10. Kendall, M. G. (1975). Rank correlation methods (4th ed.). Charles Griffin.

  11. Li, X., Zhou, Y., Asrar, G. R., Imhoff, M., & Li, X. (2018). The surface urban heat island response to urban expansion: A panel analysis for the conterminous United States. Science of the Total Environment, 605–606, 426–435.

  12. Lobell, D. B., & Gourdji, S. M. (2012). The influence of climate change on global crop productivity. Plant Physiology, 160(4), 1686–1697.

  13. LP DAAC. (2021). MOD11A2, MOD13Q1 user guides and product documentation. NASA EOSDIS Land Processes DAAC.

  14. Mildrexler, D. J., Zhao, M., & Running, S. W. (2011). A global comparison between station air temperatures and MODIS land surface temperatures reveals the cooling role of forests. Journal of Geophysical Research: Biogeosciences, 116(G3), G03025.

  15. Oke, T. R. (1982). The energetic basis of the urban heat island. Quarterly Journal of the Royal Meteorological Society, 108(455), 1–24.

Ethical Compliance & Review Process

  • All submissions are screened under plagiarism detection.
  • Review follows editorial policy.
  • Authors retain copyright.
  • Peer Review Type: Double-Blind Peer Review
  • Published on: Jun 25 2026
CCBYNC

This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt this work for non-commercial purposes with proper attribution.

View License
Scroll to Top