Jha et al. (2026) Understanding Land Use/Land Cover Dynamics and Land Surface Temperature Variations Through Machine Learning and Geospatial Approach: A Case Study of Kullu District, Himachal Pradesh, India

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Identification

• Journal: Atmosphere
• Year: 2026
• Date: 2026-09-23
• Authors: Sanjeev Jha, Nirbhav, Atul Saini, Toushif Jaman, Harish Kumar, Pradeep Kumar Jha, Fahdah Falah Ben Hasher, Abinash SİLWAL, Mohamed Zhran
• DOI: 10.3390/atmos17100916

Research Groups

• Indian Institute of Technology (IIT) Mandi
• Department of Geoinformatics, Himachal Pradesh University

Short Summary

This study evaluates machine learning-based classification methods to map land use/land cover changes in Kullu district, India, using Landsat data from 1991 to 2022. The results highlight substantial transformations and spatial differences in surface temperature.

Objective

• Evaluate the effectiveness of Maximum Likelihood Classification (MLC), Support Vector Machine (SVM), and Random Forest (RF) for mapping land use/land cover changes in Kullu district, India.

Study Configuration

• Spatial Scale: District-level analysis in Kullu district, Himachal Pradesh, India.
• Temporal Scale: 1991 to 2022 Landsat data analysis.

Methodology and Data

• Models used: Maximum Likelihood Classification (MLC), Support Vector Machine (SVM), Random Forest (RF)
• Data sources: Landsat satellite observations

Main Results

• SVM achieved the highest classification accuracy, with built-up area increasing from 4.37% in 1991 to 20.40% in 2022.
• Vegetation declined from 34.41% to 16.23%, and snow cover decreased from 15.20% to 3.02%.
• Area-weighted mean land surface temperature (LST) showed a marginal net increase of 0.08 °C over the study period.

Contributions

• This study provides valuable insights into land use/land cover transformations in the ecologically sensitive Himalayan environment.
• The findings emphasize the role of vegetation and snow cover in regulating thermal conditions, highlighting their importance for sustainable land management and spatial planning.

Funding

• This research was supported by the Department of Science and Technology (DST), Government of India (Project Code: DST/ICPS/2020/0001).
• Additional funding provided by the Indian Institute of Technology Mandi (IITM) Research Fund.

Citation

@article{Jha2026Understanding,
  author = {Jha, Sanjeev and Nirbhav and Saini, Atul and Jaman, Toushif and Kumar, Harish and Jha, Pradeep Kumar and Hasher, Fahdah Falah Ben and SİLWAL, Abinash and Zhran, Mohamed},
  title = {Understanding Land Use/Land Cover Dynamics and Land Surface Temperature Variations Through Machine Learning and Geospatial Approach: A Case Study of Kullu District, Himachal Pradesh, India},
  journal = {Atmosphere},
  year = {2026},
  doi = {10.3390/atmos17100916},
  url = {https://doi.org/10.3390/atmos17100916}
}