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Radiocarbon ages for samples of organic material within and overlying the highest Holocene floodplain and fan terraces in Glen Feshie and Glen Tromie imply sediment aggradation after ∼4.3 cal ka and probably incision after ∼3.7 cal ka, and in the Edendon Valley aggradation after ∼2.8 cal ka, with incision after ∼2.7 cal ka. The timing of sediment aggradation at all three sites postdates the onset of pine forest decline (∼4.8 cal ka) at nearby high-level sites, and coincides with wet periods characterised by high water tables. This coincidence in timing suggests that forest decline may have caused upstream extension of the tributary network, headwater incision and flashier flood responses, and that increased rainfall enhanced sediment discharge from headwater tributaries, with consequent sediment accumulation downstream on low-gradient fans and floodplains. More speculatively, exhaustion of readily entrained sediment from headwater areas may have stimulated subsequent floodplain and fan incision. Our results show that the highest Holocene terrace (the Main Holocene Terrace) is a diachronous feature, even in valleys emanating from the same upland source area, and support the conclusions of simulation models that predict marked increases in sediment discharge when deforestation is succeeded by an episode of increased rainfall. [ABSTRACT FROM AUTHOR]

Detecting forest decline is crucial for effective forest management in arid and semi-arid regions. Remote sensing using satellite image time series is useful for identifying reduced photosynthetic activity caused by defoliation. However, current studies face limitations in detecting forest decline in sparse semi-arid forests. In this study, three Landsat time-series-based approaches were used to distinguish non-declining and declining forest patches in the Zagros forests. The random forest was the most accurate approach, followed by anomaly detection and the Sen's slope approach, with an overall accuracy of 0.75 (kappa = 0.50), 0.65 (kappa = 0.30), and 0.64 (kappa = 0.30), respectively. The classification results were unaffected by the Landsat acquisition times, indicating that rather, environmental variables may have contributed to the separation of declining and non-declining areas and not the remotely sensed spectral signal of the trees. We conclude that identifying declining forest patches in semi-arid regions using Landsat data is challenging. This difficulty arises from weak vegetation signals caused by limited canopy cover before a bright soil background, which makes it challenging to detect modest degradation signals. Additional environmental variables may be necessary to compensate for these limitations. [ABSTRACT FROM AUTHOR]