OICC Press Journal of Rangeland Science 2423-642X 16 3 2026 09 30 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) 10.57647/JRS.2026.1603.25 EN Ali Mohammadian Lorestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Khorramabad, Iran Esmaeil Asadi Borojeni Natural Resources and Earth Sciences College, Shahrekord University, Shahrekord, Iran Reza SiahMansour Lorestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Khorramabad, Iran Journal Article 2026 09 30 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.