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2024
Journal Article
Title
Spatio-Temporal Transferability of Drone-Based Models to Predict Forage Supply in Drier Rangelands
Abstract
Unmanned aerial systems offer a cost-effective and reproducible method for monitoring natural resources in expansive areas. But the transferability of developed models, which are often based on single snapshots, is rarely tested. This is particularly relevant in rangelands where forage resources are inherently patchy in space and time, which may limit model transfer. Here, we investigated the accuracy of drone-based models in estimating key proxies of forage provision across two land tenure systems and between two periods of the growing season in semi-arid rangelands. We tested case-specific models and a landscape model, with the expectation that the landscape model performs better than the case-specific models as it captures the highest variability expected in the rangeland system. The landscape model did achieve the lowest error when predicting herbaceous biomass and predicted land cover with better or similar accuracy to the case-specific models. This reinforces the importance of incorporating the widest variation of conditions in predictive models. This study contributes to understanding model transferability in drier rangeland systems characterized by spatial and temporal heterogeneity. By advancing the integration of drone technology for accurate monitoring of such dynamic ecosystems, this research contributes to sustainable rangeland management practices.
Author(s)
Open Access
Rights
CC BY 4.0: Creative Commons Attribution
Language
English
Keyword(s)
Branche: Bioeconomics
Research Line: Computer graphics (CG)
Research Line: Machine learning (ML)
LTA: Monitoring and control of processes and systems
LTA: Machine intelligence, algorithms, and data structures (incl. semantics)
Agriculture
Remote sensing
Multispectral images
Model validation and analysis
Pixel classifications