Characterization of Urban Development and associated regional effect from Time Series Analysis

Authors: George Xian*, USGS EROS Data Center
Topics: Remote Sensing, Urban Geography
Keywords: Land Cover, Urban, Landsat, Time Series
Session Type: Paper
Day: 4/6/2019
Start / End Time: 5:00 PM / 6:40 PM
Room: Balcony B, Marriott, Mezzanine Level
Presentation File: No File Uploaded


Urban land cover and land-cover change also play vital roles in ecosystem performance, hydrology, and surface energy balance. One biophysical effect associated with urban development is urban heat island (UHI), in which surface temperature in urban area is usually higher than that in the surrounding rural areas. Although the UHI effect has been shown to impact local or regional climate conditions, it also affects species dynamics in urban environments. Given that species differ in thermal preference, urban environments could shift towards species with preferences for higher temperatures. In the study, we develop an approach to quantify UHI extent and intensity through detecting both urban land-cover change and surface temperature variations in a time series analysis. We used urban land-cover data from National Land Cover Database (NLCD) and Land Change Monitoring Assessment and Projection (LCMAP) developed by U.S. Geological Survey (USGS) Earth Resources Observation and Science Center. NLCD has been developed to provide consistent land-cover products. The products are attributable to the heterogeneity and dynamics of the urban land cover, which leads to uncertainties in characterizing their types, delineating their spatial extents, detecting change trends, and quantifying their biophysical conditions. This study assessed national urban variations between 2001 and 2016 using the latest land-cover datasets. The time series Landsat Analysis Ready Data (ARD) and change products developed by the LCMAP were also used to characterize changes associated with urban land. Spatial patterns and temporal trends of urban land were analyzed and uncertainties of using ARD data were further explored.

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