Remote Sensing-Based Biomass Assessment of Hedysarum coronarium from Multispectral UAV Imagery in a Mediterranean Pasture.
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| Title: | Remote Sensing-Based Biomass Assessment of Hedysarum coronarium from Multispectral UAV Imagery in a Mediterranean Pasture. |
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| Authors: | Furnitto, Nicola1 (AUTHOR), Failla, Sabina I. G.1,2 (AUTHOR) sabina.failla@unict.it, Sottosanti, Giuseppe1,3 (AUTHOR), Avondo, Marcella1 (AUTHOR), Bognanno, Matteo2 (AUTHOR), Biondi, Luisa1,3 (AUTHOR), Ramírez-Cuesta, Juan Miguel3 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1594. 20p. |
| Subjects: | Biomass estimation, Remote sensing, Range management, Grasslands, Normalized difference vegetation index |
| Geographic Terms: | Southern Europe, Italy, Sicily (Italy) |
| Abstract: | Highlights: What are the main findings? UAV-based multispectral vegetation indices accurately estimated fresh and dry above-ground biomass in the Mediterranean "Sulla" pasture. Visible-band greenness indices were among the best-performing predictors of biomass. What are the implications of the main findings? Inter-sensor harmonization enabled the combined analysis of data collected in two different growing seasons. A simple single-index workflow may support grazing management through rapid and non-destructive biomass assessment. The accurate estimation of pasture above-ground biomass (AGB) is critical for optimizing stocking rates and ensuring the sustainable use of Mediterranean pastures. This study developed empirical models to estimate fresh (AGBfresh) and dry above-ground biomass (AGBdry) using multispectral imagery acquired by Unmanned Aerial Vehicles (UAVs) in a Hedysarum coronarium pasture in Sicily, Italy. Field biomass was destructively sampled simultaneously with UAV surveys in 28 georeferenced plots during pre- and post-grazing phases over the 2023–2024 and 2024–2025 seasons. Data were collected with a DJI Mavic 3 Multispectral (for the 2024 test) and a DJI Matrice 300 + Altum-PT (for the 2025 test) and radiometrically calibrated to surface reflectance. Because two different multispectral sensors were used across years, an inter-sensor harmonization step was applied before vegetation-index calculation. Thirty-three vegetation indices were extracted as mean values within circular buffers of 1 m radius, centered on each sample plot to accommodate GNSS/georeferencing uncertainty. For each vegetation index, linear and exponential models were calibrated using 66% of the dataset and validated on the remaining 33% to predict fresh and dry above-ground biomass, and model performance was assessed using R2 and RMSE. On the validation dataset, ARVI2 and EVI2 showed the highest explanatory power for AGBfresh (R2 = 0.89), with ARVI2 providing the lower RMSE (2047 g m−2). For AGBdry, visible-band indices such as NGRDI and GRVI were among the best performers, reaching R2 = 0.85 with RMSE = 1371 g m−2. Visible-band greenness indices were among the most competitive predictors, whereas several conventional NIR-based indices showed only moderate performance. Overall, this UAV-based multispectral approach represents a promising and interpretable tool for biomass estimation in heterogeneous Mediterranean pastures, although further validation across additional seasons and sites is required to strengthen its transferability. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? UAV-based multispectral vegetation indices accurately estimated fresh and dry above-ground biomass in the Mediterranean "Sulla" pasture. Visible-band greenness indices were among the best-performing predictors of biomass. What are the implications of the main findings? Inter-sensor harmonization enabled the combined analysis of data collected in two different growing seasons. A simple single-index workflow may support grazing management through rapid and non-destructive biomass assessment. The accurate estimation of pasture above-ground biomass (AGB) is critical for optimizing stocking rates and ensuring the sustainable use of Mediterranean pastures. This study developed empirical models to estimate fresh (AGBfresh) and dry above-ground biomass (AGBdry) using multispectral imagery acquired by Unmanned Aerial Vehicles (UAVs) in a Hedysarum coronarium pasture in Sicily, Italy. Field biomass was destructively sampled simultaneously with UAV surveys in 28 georeferenced plots during pre- and post-grazing phases over the 2023–2024 and 2024–2025 seasons. Data were collected with a DJI Mavic 3 Multispectral (for the 2024 test) and a DJI Matrice 300 + Altum-PT (for the 2025 test) and radiometrically calibrated to surface reflectance. Because two different multispectral sensors were used across years, an inter-sensor harmonization step was applied before vegetation-index calculation. Thirty-three vegetation indices were extracted as mean values within circular buffers of 1 m radius, centered on each sample plot to accommodate GNSS/georeferencing uncertainty. For each vegetation index, linear and exponential models were calibrated using 66% of the dataset and validated on the remaining 33% to predict fresh and dry above-ground biomass, and model performance was assessed using R2 and RMSE. On the validation dataset, ARVI2 and EVI2 showed the highest explanatory power for AGBfresh (R2 = 0.89), with ARVI2 providing the lower RMSE (2047 g m−2). For AGBdry, visible-band indices such as NGRDI and GRVI were among the best performers, reaching R2 = 0.85 with RMSE = 1371 g m−2. Visible-band greenness indices were among the most competitive predictors, whereas several conventional NIR-based indices showed only moderate performance. Overall, this UAV-based multispectral approach represents a promising and interpretable tool for biomass estimation in heterogeneous Mediterranean pastures, although further validation across additional seasons and sites is required to strengthen its transferability. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18101594 |
