Innovative pheno-network model in characterizing the phenological process of vegetation

Authors: Chunyuan Diao*, University of Illinois at Urbana-Champaign
Topics: Remote Sensing, Biogeography
Keywords: Remote sensing; Phenology; Time series analysis; Landsat
Session Type: Paper
Day: 4/6/2019
Start / End Time: 3:05 PM / 4:45 PM
Room: Balcony B, Marriott, Mezzanine Level
Presentation File: No File Uploaded


Plant phenology characterizes the seasonal growth of vegetation and offers insights into plant biophysical activities. Over the past decade, a suite of phenological smoothing and fitting methods have been developed to estimate the critical phenological transition dates of vegetation. The vast majority of previous work has used data from MODIS, AVHRR, and SPOT VGT, owing to their fine temporal resolution and wide-area coverage. However, phenology captured by these coarse spatial resolution images often represents the mixture of various land covers, especially in spatially heterogeneous landscapes. The finer spatial resolution Landsat provides a more appropriate platform. Yet affected by temporal revisit rates and cloud contamination, Landsat time series may only capture limited temporal phenological segments. The lack of Landsat imagery throughout the year makes the conventional time series analysis difficult. Two innovative methods were developed in this study to characterize the phenological process of vegetation with Landsat imagery. The first method is the Multiyear Spectral Angle Clustering (MSAC) model. The MSAC model leverages the Landsat images across years to temporally predict the phenology of plant species in a single year, and constructs the synthesized time series of spectral signatures to estimate critical phenological transition dates. The second method is the pheno-network model. Building upon the network theory, pheno-network model estimates the phenological process through analyzing the patterns of connection between spectral-defined nodes. These two innovative models provide unique opportunities to monitor the leaf phenological process with limited satellite imagery, and offer insights in estimating the characteristic phenology.

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