Grouping-Based Time-Series Model for Monitoring of Fall Peak Coloration Dates Using Satellite Remote Sensing Data

Abstract

Accurate monitoring of plant phenology is vital to effective understanding and prediction of the response of vegetation ecosystems to climate change. Satellite remote sensing is extensively employed to monitor vegetation phenology. However, fall phenology, such as peak foliage coloration, is less well understood compared with spring phenological events, and is mainly determined using the vegetation index (VI) time-series. Each VI only emphasizes a single vegetation property. Thus, selecting suitable VIs and taking advantage of multiple spectral signatures to detect phenological events is challenging. In this study, a novel grouping-based time-series approach for satellite remote sensing was proposed, and a wide range of spectral wavelengths was considered to monitor the complex fall foliage coloration process with simultaneous changes in multiple vegetation properties. The spatial and temporal scale effects of satellite data were reduced to form a reliable remote sensing time-series, which was then divided into groups, namely pre-transition, transition and post-transition groups, to represent vegetation dynamics. The transition period of leaf coloration was correspondingly determined to divisions with the smallest intra-group and largest inter-group distances. Preliminary results using a time-series of Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2002 to 2013 at the Harvard Forest (spatial scale: ~3500 m; temporal scale: ~8 days) demonstrated that the method can accurately determine the coloration period (correlation coefficient: 0.88; mean absolute difference: 3.38 days), and that the peak coloration periods displayed a shifting trend to earlier dates. The grouping-based approach shows considerable potential in phenological monitoring using satellite time-series.

Remote sensing of phenology: Towards the comprehensive indicators of plant community dynamics from species to regional scales

“…Most relevant applications to date have been limited to classification‐based mapping of flowering events (Duan et al, 2021; Landmann et al, 2015), while direct spectral indicators of blooms (Chen et al, 2019; Mahmud et al, 2020) and fruiting phases are rare. Under‐studied aspects of foliar seasonality, such as senescence and late‐season coloration, may also hold important clues on plant community diversity and adaptations to environmental changes (Gallinat et al, 2015; Xie et al, 2018; Zhou et al, 2020) and, similarly, require unique approaches for their detection that may be enabled by novel instruments and datasets.…”