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  4. Spatio-Temporal Transferability of Drone-Based Models to Predict Forage Supply in Drier Rangelands
 
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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)
Amputu, Vistorina
Univ. Tübingen  
Männer, Florian
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Tielbörger, Katja
Univ. Tübingen  
Knox, Nichola
Downforce Technologies
Journal
Remote sensing  
Project(s)
BioTip: Desertifikations-Kipppunkte verstehen und bewältigen - eine namibische Perspektive (NamTip) - Teilprojekt 2: Primärproduktion und Boden-Samenbank  
Funder
Bundesministerium für Bildung und Forschung -BMBF-  
Open Access
File(s)
Download (10.79 MB)
Rights
CC BY 4.0: Creative Commons Attribution
DOI
10.3390/rs16111842
10.24406/publica-3455
Additional full text version
Landing Page
Language
English
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
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

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