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• W4225245156 abstract "Abstract Aims Ecological restoration is integral to meeting conservation goals in rapidly changing landscapes, but outcomes vary substantially with some projects failing to meet their targets. To understand the causes of this variability, long‐term monitoring of existing projects is critical, but this comes at considerable costs. Current literature counts several studies using time series of satellite images to assess vegetation responses to disturbances and landscape transformations. Yet such methods are seldom used in the restoration literature and in practice. This synthesis seeks to identify how common remote sensing approaches for the assessment of plant recovery could inform the monitoring and management of restored plant communities. Methods This paper reviews the methods and metrics used to detect trajectories (i.e., change through time) in plant properties from rich time series of aerial and satellite images following change drivers including fire, extreme climatic events, climate change, and pest outbreaks. Specifically, it reviews the sensors, vegetation properties, modeling approaches, and indicators that can help measure plant stress and response to interventions. Results and Conclusions Remote sensing methods commonly used in disturbance ecology and assessments of land‐cover changes could inform the monitoring of restoration projects at low cost and over large spatio‐temporal scales, thus bridging the gap between field surveys to rapidly identify stressors or unexpected vegetation responses. Potential applications include comparing sites to identify factors impacting their responses to restoration, assessing restoration success, and testing ecological hypotheses to guide future project planning and design." @default.
• W4225245156 created "2022-05-03" @default.
• W4225245156 creator A5020403050 @default.
• W4225245156 date "2022-04-01" @default.
• W4225245156 modified "2023-09-30" @default.
• W4225245156 title "Leveraging time series of satellite and aerial images to promote the long‐term monitoring of restored plant communities" @default.
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• W4225245156 doi "https://doi.org/10.1111/avsc.12664" @default.
• W4225245156 hasPublicationYear "2022" @default.
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