Mapping Global Impervious Cover from Landsat Data

Eric Brown de Colstoun, NASA GSFC, eric.c.browndecolsto@nasa.gov (Presenter)
Chengquan Huang, University of Maryland, cqhuang@umd.edu
Bin Tan, NASA GSFC, bin.tan@nasa.gov
Panshi Wang, University of Maryland, pswang@umd.edu (Presenter)
Tilton James, NASA GSFC, james.c.tilton@nasa.gov
Phillips Jacqueline, USRA/NASA GSFC, jacqueline.phillips@nasa.gov
Robert E Wolfe, NASA GSFC, robert.e.wolfe@nasa.gov
Sarah Smith, SSAI NASA GSFC, sarah.e.smith@nasa.gov

Over half the worlds population lives in cities and the processes of associated urbanization are often permanent. Most of the current estimates of urbanization are made at scales of km or at least several hundred meters and fail to capture the fine detail associated with this human modification of the landscape. New Global Landsat data archives provide the opportunity to map and monitor urbanization at an unprecedented level of detail. We are producing the first 30m, global scale estimates of impervious cover using the Global Land Survey (GLS) Landsat data and will present some of our continental scale results for the project. We have developed a massive training data set derived using the non-classified archive of commercial satellite data from the National Geospatial Intelligence Agency (NGA) using in-house image segmentation tools and used these data to train a continental regression tree algorithm that produces results at 30m using the GLS data corrected to surface reflectance. An image segmentation approach is also used directly on the GLS data to filter errors of commission on surfaces that are spectrally similar to impervious cover. Our approach is applied to the 2010 GLS archive and will be applied to the 2000 GLS data to estimate impervious cover change. The approach also lends itself to future application with GLS and other Landsat 8 data.

Presentation Type:  Poster

Session:  Theme 4: Human influence on global ecosystems   (Mon 4:30 PM)

Associated Project(s): 

• Brown de Colstoun, Eric: Using Landsat Global Land Survey Data to Measure and Monitor Worldwide Urbanization ...details
• Huang, Cheng: Using Landsat Global Land Survey Data to Measure and Monitor Worldwide Urbanization ...details

Poster Location ID: 35