10.57647/JRS.2026.1603.25

Assessment of Integrated Landsat-8 and Sentinel-2 Images on Identification and Resolution of the Burned Rangelands (Case Study: Semi-steppe Rangelands in Chahar-Mahal Bakhtiari, Province, Iran)

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  • Ali Mohammadian*1,
  • Esmaeil Asadi Borojeni2,
  • Reza SiahMansour1,
  1. Lorestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Khorramabad, Iran
  2. Natural Resources and Earth Sciences College, Shahrekord University, Shahrekord, Iran

Received: 2025-03-26

Revised: 2025-11-20

Accepted: 2025-12-19

Published in Issue 2026-09-30

Copyright (c) 2026 Ali Mohammadian, Esmaeil Asadi Borojeni, Reza SiahMansour (Author)

Creative Commons License

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

How to Cite

Mohammadian, A., Asadi Borojeni, E., & SiahMansour, R. (2026). Assessment of Integrated Landsat-8 and Sentinel-2 Images on Identification and Resolution of the Burned Rangelands (Case Study: Semi-steppe Rangelands in Chahar-Mahal Bakhtiari, Province, Iran). Journal of Rangeland Science, 16(3). https://doi.org/10.57647/JRS.2026.1603.25
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Abstract

Accurate monitoring of the combined impacts of fire and grazing on plant communities in semi-steppe rangelands is challenging due to spatial and temporal variability. This study demonstrates that integrating multi-sensor remote sensing data can significantly enhance monitoring precision.The Gram-Schmidt algorithm was employed to fuse Landsat-8 and Sentinel-2 imagery, improving spatial resolution from 30m to 10m while preserving spectral characteristics. In addition, pan-sharpening techniques enhanced Landsat-8 imagery to 15 m resolution. Classification of affected areas was performed using Maximum Likelihood Classification (MLC) with a comprehensive dataset including spectral bands, the Normalized Burn Ratio Thermal (NBRT) index, Tasseled Cap Brightness (TC-B), Digital Elevation Model (DEM), and Principal Component Analysis (PCA). Our findings revealed a strong positive correlation between spatial resolution and classification accuracy. The 10m resolution imagery achieved superior performance (71% overall accuracy, Kappa = 0.66), effectively discriminated fire-affected areas across different age classes (1-3 and 3-5 years post-fire) under varying grazing intensities. The 30m resolution data showed significantly low accuracy (39% overall accuracy, Kappa = 0.34). Higher-resolution imagery substantially reduced salt-and-pepper noise and enhanced visual interpretability. This research confirmed that integrated multi-sensor data processing provides a robust approach for monitoring rangeland dynamics. The methodology offers valuable capabilities for mapping fire-affected vegetation and supports management strategies in extensive, topographically challenging environments where traditional field-based methods are impractical.

Keywords

  • Fusion,
  • Burned pasture,
  • Gram-Schmidt,
  • Grazing intensity,
  • Classification
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