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Soureshjani Samani R, Naghipour A A, Tahmasbi Kohyani P, Heidari Ghahfarrokhi Z. Application of Vegetation Indices and Image Differencing of Landsat-8 and Sentinel-2 for Estimating Production in Semi-Steppe Rangelands. مرتع 2026; 20 (1)
URL: http://rangelandsrm.ir/article-1-1343-en.html
URL: http://rangelandsrm.ir/article-1-1343-en.html
Razieh Soureshjani Samani 
, Ali asghar Naghipour , Pejman Tahmasbi Kohyani , Zahra Heidari Ghahfarrokhi
, Ali asghar Naghipour , Pejman Tahmasbi Kohyani , Zahra Heidari Ghahfarrokhi
Abstract: (18 Views)
Background and Objectives: Semi-steppe rangelands of Iran play a crucial role in providing forage, conserving biodiversity, and supporting ecosystem services. Accurate estimation of forage production and monitoring vegetation changes is essential for sustainable rangeland management. Traditional methods are labor-intensive and costly, whereas remote sensing and vegetation indices offer efficient alternatives. This study aimed to evaluate the performance of Landsat-8 and Sentinel-2 imagery, combined with image differencing techniques, for estimating rangeland production and monitoring vegetation dynamics in the Tang-e-Sayyad protected area, located in Chaharmahal va Bakhtiari province.
Materials and Methods: Field sampling was conducted in eight sites during two phenological stages. Forage production of five plant growth form (annual grasses, annual forbs, perennial grasses, perennial forbs, and shrubs) was measured using the double-sampling method. Simultaneously, Landsat-8 OLI and Sentinel-2 MSI images with minimum cloud cover were acquired. After preprocessing, a set of vegetation indices (e.g., NDVI, EVI, SAVI, PVI, DVI, MSAVI) was calculated. Image differencing was then applied between multi-temporal images to extract vegetation and production changes. The relationships between remote sensing indices and field data were analyzed using Pearson correlation in R software.
Results: The results revealed that Landsat-8 imagery performed better than Sentinel-2 in estimating forage production. Among vegetation indices, PVI1 and DVI showed the highest correlations (≈0.64) with the combined production of perennial forbs and perennial grasses in June. MSAVI1 and MSAVI2 demonstrated robustness under varying vegetation cover and soil background conditions. For annual forms, EVI had the strongest predictive power, while in shrublands, only EVI exhibited significant correlations. In general, most vegetation indices were able to reflect production dynamics, although their performance varied depending on phenological stage and plant functional type.
Conclusion: This study highlighted the potential of integrating vegetation indices with image differencing as a cost-effective, rapid, and accurate approach for monitoring production changes in semi-steppe rangelands. The proposed method enables identification of areas with increasing or declining forage production, thereby supporting sustainable rangeland management. For future studies, incorporating optical and radar datasets is recommended to overcome limitations such as cloud cover and index saturation, thereby improving monitoring accuracy.
Materials and Methods: Field sampling was conducted in eight sites during two phenological stages. Forage production of five plant growth form (annual grasses, annual forbs, perennial grasses, perennial forbs, and shrubs) was measured using the double-sampling method. Simultaneously, Landsat-8 OLI and Sentinel-2 MSI images with minimum cloud cover were acquired. After preprocessing, a set of vegetation indices (e.g., NDVI, EVI, SAVI, PVI, DVI, MSAVI) was calculated. Image differencing was then applied between multi-temporal images to extract vegetation and production changes. The relationships between remote sensing indices and field data were analyzed using Pearson correlation in R software.
Results: The results revealed that Landsat-8 imagery performed better than Sentinel-2 in estimating forage production. Among vegetation indices, PVI1 and DVI showed the highest correlations (≈0.64) with the combined production of perennial forbs and perennial grasses in June. MSAVI1 and MSAVI2 demonstrated robustness under varying vegetation cover and soil background conditions. For annual forms, EVI had the strongest predictive power, while in shrublands, only EVI exhibited significant correlations. In general, most vegetation indices were able to reflect production dynamics, although their performance varied depending on phenological stage and plant functional type.
Conclusion: This study highlighted the potential of integrating vegetation indices with image differencing as a cost-effective, rapid, and accurate approach for monitoring production changes in semi-steppe rangelands. The proposed method enables identification of areas with increasing or declining forage production, thereby supporting sustainable rangeland management. For future studies, incorporating optical and radar datasets is recommended to overcome limitations such as cloud cover and index saturation, thereby improving monitoring accuracy.
Type of Study: Research |
Subject:
Special
Received: 2025/09/1 | Accepted: 2026/02/22 | Published: 2026/04/4
Received: 2025/09/1 | Accepted: 2026/02/22 | Published: 2026/04/4
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