IEEE Geoscience and Remote Sensing Society (GRSS) Washington DC/Northern VA Chapter Webinar Seminar on April 25, 2019

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Data and Algorithms for Mapping and Monitoring Urban Impervious Surface through Earth Observation

Chengbin Deng

Department of Geography

State University of New York at Binghamton

 

Abstract:

Urban impervious cover information is essential for urban and environmental applications at the regional/national scales, such as metropolitan master planning, watershed conservation and restoration. A variety of image processing algorithms (e.g., spectral mixture analysis, or SMA, and machine learning approaches) have been developed to derive urban impervious surface information from satellite data. These algorithms have their own limitations which prevent wider applications, and it is necessary to accommodate these limitations for better urban impervious surface mapping. In this presentation, recent efforts and progress will be present. When mapping subpixel urban impervious surface at one point in time, spatially adaptive SMA (SASMA) will be introduced to improve the performance of existing spectral unmixing. When monitoring the dynamic of subpixel urban impervious surface over time, we further develop a Continuous Subpixel Mapping (CSM) method using Landsat time series. The integration of subpixel mapping and time series analysis in CSM can not only derive percent change of urban impervious surface of any time interval, but detect different urban transition types (urban expansion, urban shrinkage and surface modifications). Challenges and opportunities for urban mapping in the future will also be discussed.

 

 



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  • Greenbelt, Maryland
  • United States

Staticmap?size=250x200&sensor=false&zoom=14&markers=38.9937333%2c 76
  • Starts 04 April 2019 12:58 PM
  • Ends 25 April 2019 12:58 PM
  • All times are US/Eastern
  • No Admission Charge
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  Speakers

Chengbin Deng of Department of Geography, State University of New York at Binghamton

Topic:

Data and Algorithms for Mapping and Monitoring Urban Impervious Surface through Earth Observation

Urban impervious cover information is essential for urban and environmental applications at the regional/national scales, such as metropolitan master planning, watershed conservation and restoration. A variety of image processing algorithms (e.g., spectral mixture analysis, or SMA, and machine learning approaches) have been developed to derive urban impervious surface information from satellite data. These algorithms have their own limitations which prevent wider applications, and it is necessary to accommodate these limitations for better urban impervious surface mapping. In this presentation, recent efforts and progress will be present. When mapping subpixel urban impervious surface at one point in time, spatially adaptive SMA (SASMA) will be introduced to improve the performance of existing spectral unmixing. When monitoring the dynamic of subpixel urban impervious surface over time, we further develop a Continuous Subpixel Mapping (CSM) method using Landsat time series. The integration of subpixel mapping and time series analysis in CSM can not only derive percent change of urban impervious surface of any time interval, but detect different urban transition types (urban expansion, urban shrinkage and surface modifications). Challenges and opportunities for urban mapping in the future will also be discussed.

Biography:

Dr. Chengbin Deng is an Assistant Professor of geography at State University of New York at Binghamton. His research focuses on remote sensing image processing, land use and land cover change, time series analysis, machine learning and data driven methods, geospatial big data, and their applications in urban/nautral environment. Dr. Deng is the author of 23 peer-reviewed journal articles (14 first authored and 3 single authored) and 2 invited book chapters. His research has been published in various top journals in remote sensing, geography, and environment studies. Among them, two papers are labeled as the “hottest journal” (top 25 most downloaded papers), published in Remote Sensing of Environment, and ISPRS Journal of Photogrammetry and Remote Sensing, respectively. He has served as an invited reviewer for a variety of academic journals. Due to his significant contributions, he was awarded as an Outstanding Reviewer of three Elsevier journals: ISPRS Journal of Photogrammetry and Remote Sensing in 2015, Remote Sensing of Environment in 2016, and Landscape and Urban Planning in 2017. Dr. Deng will be promoted to Associate Professor with tenure effective fall 2019.

Email:

Address:Department of Geography, State University of New York at Binghamton,