Masoud Safari; Marzban Faramarzi; REza Omidpour; Hassan Fathizad
Volume 32, Issue 2 , July 2025, , Pages 140-159
Abstract
Background and ObjectiveVegetation cover and aboveground biomass are critical components of arid and semi-arid ecosystems, playing a vital role in environmental sustainability, carbon sequestration, biodiversity conservation, and providing forage for both domestic and wild herbivores. Due to their high ...
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Background and ObjectiveVegetation cover and aboveground biomass are critical components of arid and semi-arid ecosystems, playing a vital role in environmental sustainability, carbon sequestration, biodiversity conservation, and providing forage for both domestic and wild herbivores. Due to their high sensitivity to environmental changes, these regions require accurate and continuous monitoring. In recent decades, the use of vegetation indices derived from remote sensing data has emerged as a promising approach for modeling vegetation cover and biomass in natural resource management. However, these indices have been less thoroughly evaluated in arid and semi-arid areas. This study aims to assess the efficiency of Landsat 8 and Sentinel-2 satellite imagery and various vegetation indices in modeling vegetation cover and aboveground biomass in the arid rangelands of western Iran, using linear regression methods.Materials and MethodsThis study was conducted in the arid rangelands of Ilam Province, western Iran. Field data were collected using a random-systematic sampling method across 50 plots (30 × 30 meters) during the peak growing season and prior to livestock grazing. In each plot, the percentage of live vegetation cover, litter, bare soil, and gravel was recorded. Aboveground biomass was measured by clipping vegetation at ground level and weighing the oven-dried samples. Satellite images from Landsat 8 and Sentinel-2, corresponding to the same period as the field sampling, were acquired. Both slope-based vegetation indices, such as NDVI and EVI, and distance-based indices, such as MSAVI and SAVI, were extracted using ENVI and ArcGIS software. Linear regression analysis was employed to model vegetation cover and aboveground biomass. The performance of the selected indices was evaluated using the coefficient of determination (R²) and root mean square error (RMSE).ResultsThe results indicated that distance-based vegetation indices, such as MSAVI, outperformed slope-based indices in modeling vegetation cover and aboveground biomass. The MSAVI index derived from Landsat imagery provided the highest accuracy, with an R² of 0.19 for vegetation cover and 0.21 for biomass. Linear regression modeling showed that vegetation cover in the study area ranged from 5% to 81%. Additionally, the estimated aboveground biomass ranged from 6.8 to 58.4 grams per square meter (gm²). According to the results, when using slope-based indices such as NDVI, Sentinel-2 imagery yielded more accurate vegetation cover modeling compared to Landsat imagery. However, when distance-based indices were applied, Landsat imagery delivered significantly higher accuracy, while the accuracy of Sentinel-2 images decreased relative to their performance with slope-based indices.ConclusionThe results of this study indicated that the accuracy of vegetation cover modeling is influenced by both the types of indices used and the spatial resolution of satellite data. For slope-based indices like the Normalized Difference Vegetation Index (NDVI), Sentinel imagery demonstrated higher accuracy than Landsat for estimating vegetation cover. Conversely, for distance-based indices, Landsat imagery provided better accuracy, while the precision of Sentinel imagery decreased under these conditions. These findings emphasize the importance of selecting appropriate spectral indices tailored to specific regional characteristics, as this choice can significantly impact modeling accuracy. Furthermore, the results highlight that in arid regions, distance-based indices may be more effective, as they mitigate the impact of bare soil reflectance and provide more reliable information about vegetation cover. This research offers a foundation for optimizing remote sensing methodologies in future studies.
zhila ghorbani; Kiumars Sefidi; Mahshid Souri; Mehdi Moameri
Volume 28, Issue 3 , October 2021, , Pages 395-409
Abstract
Awareness of the extent and impact of vegetation from livestock grazing disorders can be a solution to develop rangeland ecosystem management strategies to achieve sustainability and continuous production in these ecosystems. In this study, the production of aerial and underground organs of grazable ...
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Awareness of the extent and impact of vegetation from livestock grazing disorders can be a solution to develop rangeland ecosystem management strategies to achieve sustainability and continuous production in these ecosystems. In this study, the production of aerial and underground organs of grazable livestock species in the southeastern rangelands of Sabalan under the influence of different grazing intensities and distance from the village as the focus of the crisis was investigated. In addition, the development and evaluation of ANFIS model was presented in order to predict the production of aerial and underground organs of food species and compare the results with the regression model. For evaluation of regression and ANFIS models the Root Mean Square Error (RMSE) and correlation coefficient (R2) were used. The results showed that different grazing intensities, distance from village and interaction between them were significant effect on the production of aerial and underground organs of palatability species at (p≤0.01). Also, with increasing grazing intensity, the production of aerial and underground organs of these species decreased. The results of ANFIS section showed that in low grazing intensity and distance of about 400 meters, the highest amount of production of palatable species is observed. The lowest production of these species is predicted to be close to the village (200 meters). Moreover, the highest amount of underground biomass at farther distances (600 meter) and lowest amount of that was observed at 200 meter. In addition, ANFIS model with higher accuracy (R2 = 0.98 and R2 = 0.95) and lower error (RMSE = 0.9792 and RMSE = 1.168) than less accurate regression model (R2 = 0.92 and R2 0.77) which also had more errors (RMSE = 2.2835 and RMSE = 3.8954), predicted the production of aerial and underground organs, respectively.