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

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State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
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School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
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Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(2), 274; https://doi.org/10.3390/rs12020274
Received: 18 December 2019 / Revised: 7 January 2020 / Accepted: 12 January 2020 / Published: 14 January 2020
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. View Full-Text
Keywords: phenology monitoring; fall foliage coloration; grouping-based time-series analysis; spatial and temporal scale; optical remote sensing phenology monitoring; fall foliage coloration; grouping-based time-series analysis; spatial and temporal scale; optical remote sensing
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MDPI and ACS Style

Zhou, Q.; Sun, X.; Tian, L.; Li, J.; Li, W. Grouping-Based Time-Series Model for Monitoring of Fall Peak Coloration Dates Using Satellite Remote Sensing Data. Remote Sens. 2020, 12, 274.

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