UAV and satellite-based sensing to map ecological sites at the landscape scale

Authors: PONCE-CAMPOS, G.E. - University Of Arizona; MCCLARAN, M. - University Of Arizona; Heilman, Philip - Phil; GILLAN, J.K. - University Of Arizona

Submitted to: Open Journal of Ecology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/25/2023
Publication Date: 8/28/2023
Citation: Ponce-Campos, G., McClaran, M., Heilman, P., Gillan, J. 2023. UAV and satellite-based sensing to map ecological sites at the landscape scale. Open Journal of Ecology. 13(8):560-596. https://doi.org/10.4236/oje.2023.138035.
DOI: https://doi.org/10.4236/oje.2023.138035

Interpretive Summary: The state-of-the-art conceptual model of rangeland management emphasizes managing for alternatives states within the potential vegetation communities an ecological site can support. However, ecological states are rarely mapped, so this conceptual model is difficult to apply. This study presents an innovative approach for mapping two ecological states, Large Shrub Grass (LSG) and Large Shrub Eroded (LSE), within the Sandy Loam Upland and Deep (SLUD) ecological sites using a combination of drone and satellite data. The method uses the Largest Patch Index (LPI) as a proxy metric to estimate eroded areas and classify ecological states using both drone and satellite-based remote sensing. The study demonstrates the potential of this methodology by generating spatial layers at the landscape. The accuracy of the classification process is evaluated by comparing the results to ground-based polygons, dirt roads, and water locations. While the model performs well in identifying eroded areas, misclassifications occur in regions with mixed vegetation cover or low biomass. Future research should focus on incorporating temporal information from historical remote sensing archives to improve understanding of ecological state dynamics. Further work should also refine the approach through additional validation and exploring new remote sensing datasets.

Technical Abstract: Mapping ecological states in semi-arid rangelands is crucial for effective land management and conservation efforts because it identifies difference in the ecological conditions across a landscape. This study presents an innovative approach for mapping two ecological states, Large Shrub Grass (LSG) and Large Shrub Eroded (LSE), within the Sandy Loam Upland and Deep (SLUD) ecological sites using a combination of drone and satellite data. The methodology leverages the Largest Patch Index (LPI) as a proxy metric to estimate eroded areas and classify ecological states. The integration of unmanned aerial vehicle (UAV) data with satellite-based remote sensing provides a scalable approach that can benefit various stakeholders involved in rangeland management. By utilizing the bare-ground LPI metric, which indicates the connectedness of bare-ground, the methodology enables the classification of ecological states at a regional scale. The accuracy of the classification process is evaluated by comparing the results to ground-based polygons, dirt roads, and water locations. While the model performs well in identifying eroded areas, misclassifications occur in regions with mixed vegetation cover or low biomass. Future research should focus on incorporating temporal information from historical remote sensing archives to improve understanding of ecological state dynamics. Additionally, validation efforts can be enhanced by incorporating more ground-truth data and testing the methodology in diverse rangeland areas. Further work should refine the approach through additional validation and exploring new remote sensing datasets. The methodology can be replicated in other regions to inform land management decisions, promote sustainable resource use, and advance the field of ecological state mapping.