Characterizing spatiotemporal dynamics in phenology of urban ecosystems based on Landsat data

Authors: Xuecao Li*, Iowa State University, Yuyu Zhou, Iowa State University, Ghassem R Asrar, Joint Global Change Research Institute of Pacific Northwest National Laboratory, Lin Meng, Iowa State University
Topics: Coupled Human and Natural Systems
Keywords: Urban systems; Landsat; Double sigmoid; Vegetation phenology; Urbanization
Session Type: Paper
Day: 4/14/2018
Start / End Time: 2:00 PM / 3:40 PM
Room: Studio 2, Marriott, 2nd Floor
Presentation File: No File Uploaded


In urban environments, vegetation phenology is important because of its influence on public health, and energy demand. In this study, we studied the potential use of remotely sensed observations to derive some phenology indicators for vegetation embedded within the urban core domains in four distinctly different U.S. regions during the past three decades. We used all available Landsat observations (circa 3000 scenes) from 1982 to 2015 and a self-adjusting double logistic model to detect and quantify the annual change of vegetation phenophases. The proposed model can capture and quantify not only phenophases of dense vegetation in rural areas, but also those of mixed vegetation in urban core domains. The derived phenology indicators show a good agreement with similar indicators derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and in situ observations. The vegetation phenology and its seasonal and interannual dynamics demonstrate a distinct spatial pattern in urban domains with an earlier (9–14 days) start-of-season (SOS) and a later (13–20 days) end-of-season (EOS), resulting in an extended (5–30 days) growing season length (GSL) when compared to the surrounding suburban and rural areas in the four study regions. There is a general long-term trend of decreasing SOS (−0.30 day per year), and increasing EOS and GSL (0.50 and 0.90 day per year, respectively) over past three decades for these study regions. The Landsat derived phenology information for urban domains provides more details when compared to the coarse-resolution datasets such as MODIS, thus improves our understanding of human-natural systems interactions in urban domains.

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