Version 1
: Received: 5 August 2024 / Approved: 6 August 2024 / Online: 6 August 2024 (12:11:17 CEST)
How to cite:
Ogungbuyi, M. G.; Mohammed, C.; Fischer, A. M.; Turner, D.; Whitehead, J.; Harrison, M. T. Enhancing Grassland Management Sustainability Through Drone And Satellite: A Comparative Study. Preprints2024, 2024080372. https://doi.org/10.20944/preprints202408.0372.v1
Ogungbuyi, M. G.; Mohammed, C.; Fischer, A. M.; Turner, D.; Whitehead, J.; Harrison, M. T. Enhancing Grassland Management Sustainability Through Drone And Satellite: A Comparative Study. Preprints 2024, 2024080372. https://doi.org/10.20944/preprints202408.0372.v1
Ogungbuyi, M. G.; Mohammed, C.; Fischer, A. M.; Turner, D.; Whitehead, J.; Harrison, M. T. Enhancing Grassland Management Sustainability Through Drone And Satellite: A Comparative Study. Preprints2024, 2024080372. https://doi.org/10.20944/preprints202408.0372.v1
APA Style
Ogungbuyi, M. G., Mohammed, C., Fischer, A. M., Turner, D., Whitehead, J., & Harrison, M. T. (2024). Enhancing Grassland Management Sustainability Through Drone And Satellite: A Comparative Study. Preprints. https://doi.org/10.20944/preprints202408.0372.v1
Chicago/Turabian Style
Ogungbuyi, M. G., Jason Whitehead and Matthew Tom Harrison. 2024 "Enhancing Grassland Management Sustainability Through Drone And Satellite: A Comparative Study" Preprints. https://doi.org/10.20944/preprints202408.0372.v1
Abstract
This study evaluates the use of remote sensing to improve real-time adaptive management of grasslands. By quantifying grassland biomass through changes in sward height from pre- and post-grazing events using unmanned aerial systems (UAS), we derived accurate biomass estimates. Additionally, we aimed to enhance the biomass estimation with spectral data from Sentinel-2 imagery, which is often hindered by cloud contamination, by combining it with a random forest algorithm. The calibration of UAS biomass using field measurements from sward height changes through 3D photogrammetry produced strong regression metrics (R2= 0.75, RMSE= 1240 kg DM/ha and MAE=980 kg DM/ha). Integrating this UAS-derived data with Sentinel-2 imagery, the satellite based biomass model (R2= 0.56, RMSE=2140 kg DM/ha and MAE=1585 kg DM/ha) compared to using the Sentinel-2 random forest-enabled model alone (R2= 0.56, RMSE=2140 kg DM/ha and MAE=1585 kg DM/ha). This study emphasise the importance of timely and accurate biomass estimation for adaptive gazing management at the field level, highlighting the balance between utilising advanced remote sensing technologies and addressing operational complexities. By optimising the integration of UAS and satellite data, we aim to enhance grassland utilisation, ecosystem functions, nutrient cycling, and sustainability in land use.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
