This study used two modelling approaches to predict future urban landscape for the Baltimore-Washington metropolitan areas. In the first approach, we implemented traditional SLEUTH urban simulation model by using publicly available and locally-developed land cover and transportation data. Historical land cover data from 1996, 2001, 2006, and 2011 were used to calibrate SLEUTH model and predict urban growth from 2011 to 2070. SLEUTH model achieved 94.9% of overall accuracy for a validation year of 2014. For the second modelling approach, we predicted future county-level population (e.g., 2050) using historical population data and time-series forecasting. We then used future population projection of 2050, aided by strong population-imperviousness statistical relationship (R2, 0.78-0.86), to predict total impervious surface area for each county. These population-predicted total impervious surface areas were compared to SLEUTH model output, at the county-aggregated spatial scale. For most counties, SLEUTH generated substantially higher number of impervious pixels. An annual urban growth rate of 6.24% for SLEUTH model was much higher than the population-based approach (1.33%), suggesting a large discrepancy between these two modelling approaches. The SLEUTH simulation model, although achieved high accuracy for 2014 validation, may have over-predicted urban growth for our study area. For population-predicted impervious surface area, we further developed a lookup table approach to integrate SLEUTH out and generated spatially explicit urban map for 2050. This lookup table approach has high potential to integrate population-predicted and SLEUTH-predicted urban landscape, especially when future population can be predicted with reasonable accuracy. / Master of Science
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/73649 |
| Date | 18 June 2015 |
| Creators | Zhao, Suwen |
| Contributors | Geography, Shao, Yang, Prisley, Stephen P., Campbell, James B. Jr. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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