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• W4386364976 abstract "Forest fragmentation has been increasingly exacerbated by deforestation, urbanization, and agricultural expansion. Monitoring the forest fragments via the lens of tree-crown scale leaf phenology is critical to understand tree species phenological responses to climate change and identify the fragment species vulnerable to environmental disturbance. Despite advances in remote sensing for phenology monitoring, detecting tree-crown scale leaf phenology in fragmented forests remains challenging. Simultaneous tracking of key spring phenological events that are crucial to ecosystem functions and climate change responses is also neglected. To address these challenges, we develop a novel tree-crown scale remote sensing phenological monitoring framework to characterize all the critical spring phenological events of individual trees of deciduous forest fragments, with Trelease Woods in Champaign, Illinois as a case study. The novel framework comprises four components: 1) generate high spatiotemporal resolution fusion imagery from multi-scale satellite time series with a hybrid deep learning fusion model; 2) calibrate PlanetScope imagery time series with fusion data using histogram matching; 3) model tree-crown scale phenology trajectory with a Beck logistic-based method; 4) detect a diversity of tree-crown scale phenological events using several phenological metric extraction methods (i.e., threshold- and curve feature-based methods). Combined with weekly in-situ phenological observations of 123 individual trees across 12 broadleaf species from 2017 to 2020, the framework effectively bridges the satellite- and field-based phenological measures for the key spring phenological events (i.e., budswell, budburst, leaf expansion, and leaf maturity events) at the tree-crown scale, particularly for large individuals (RMSE <1 week for most events). Calibration of PlanetScope imagery using multi-scale satellite fusion data in consideration of landscape fragmentation is critical for monitoring tree phenology of forest fragments. Compared to curve feature-based methods, threshold-based phenometric extraction methods demonstrate enhanced capability in detecting spring leaf phenological dynamics of individual trees. Among the phenological events, full leaf out and early leaf expansion events are retrieved with high accuracy using calibrated PlanetScope time series (RMSE from 3 to 5 days and R-squared higher than 0.8). With both intensive satellite and field phenological efforts, this novel framework is at the forefront of interpreting tree-crown scale remotely sensed phenological metrics in the context of biologically meaningful field phenological events in fragmented forest setting." @default.
• W4386364976 created "2023-09-02" @default.
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• W4386364976 date "2023-11-01" @default.
• W4386364976 modified "2023-10-18" @default.
• W4386364976 title "Monitoring spring leaf phenology of individual trees in a temperate forest fragment with multi-scale satellite time series" @default.
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• W4386364976 doi "https://doi.org/10.1016/j.rse.2023.113790" @default.
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