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Mapping and Assessing Urban Impervio...
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Weng, Fenqing.
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Mapping and Assessing Urban Impervious Areas Using Multiple Endmember Spectral Mixture Analysis: A Case Study in the City of Tampa, Florida.
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Mapping and Assessing Urban Impervious Areas Using Multiple Endmember Spectral Mixture Analysis: A Case Study in the City of Tampa, Florida./
作者:
Weng, Fenqing.
面頁冊數:
104 p.
附註:
Source: Masters Abstracts International, Volume: 51-02.
Contained By:
Masters Abstracts International51-02(E).
標題:
Remote Sensing. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1516156
ISBN:
9781267531322
Mapping and Assessing Urban Impervious Areas Using Multiple Endmember Spectral Mixture Analysis: A Case Study in the City of Tampa, Florida.
Weng, Fenqing.
Mapping and Assessing Urban Impervious Areas Using Multiple Endmember Spectral Mixture Analysis: A Case Study in the City of Tampa, Florida.
- 104 p.
Source: Masters Abstracts International, Volume: 51-02.
Thesis (M.A.)--University of South Florida, 2012.
The advance in remote sensing technology helps people more easily assess urban growth. In this study, the utility of multiple endmember spectral mixture analysis (MESMA) is examined in a sub-pixel analysis of Landsat Thematic Mapper (TM) imagery to map urban physical components in Tampa, FL. The three physical components of urban land cover (LC): impervious surface, vegetation and soil, were compared using the proposed MESMA with a traditional spectral mixture analysis (SMA). MESMA decomposes each pixel to address the heterogeneity of urban LC characteristic by allowing the number and types of endmembers to vary on a per pixel basis. This study generated 642 spectral mixture models of 2-, 3-, and 4-endmembers for each pixel to estimate the fractions of impervious surface, vegetation, soil, and shade in the study area with a constraint of lowest root mean square error (RMSE). A comparative analysis of the impervious surface areas (ISA) mapped with MESMA and SMA demonstrated that MESMA produced more accurate results of mapping urban physical components than those by SMA. With the multiyear Landsat TM data, we quantified sub-pixel %ISA and the %ISA changes to assess urban growth in the City of Tampa, Florida during the past twenty years. The experimental results demonstrate that the MESMA approach is effective in mapping and monitoring urban land use/land cover changes using moderate-resolution multispectral imagery at a sub-pixel level.
ISBN: 9781267531322Subjects--Topical Terms:
1018559
Remote Sensing.
Mapping and Assessing Urban Impervious Areas Using Multiple Endmember Spectral Mixture Analysis: A Case Study in the City of Tampa, Florida.
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The advance in remote sensing technology helps people more easily assess urban growth. In this study, the utility of multiple endmember spectral mixture analysis (MESMA) is examined in a sub-pixel analysis of Landsat Thematic Mapper (TM) imagery to map urban physical components in Tampa, FL. The three physical components of urban land cover (LC): impervious surface, vegetation and soil, were compared using the proposed MESMA with a traditional spectral mixture analysis (SMA). MESMA decomposes each pixel to address the heterogeneity of urban LC characteristic by allowing the number and types of endmembers to vary on a per pixel basis. This study generated 642 spectral mixture models of 2-, 3-, and 4-endmembers for each pixel to estimate the fractions of impervious surface, vegetation, soil, and shade in the study area with a constraint of lowest root mean square error (RMSE). A comparative analysis of the impervious surface areas (ISA) mapped with MESMA and SMA demonstrated that MESMA produced more accurate results of mapping urban physical components than those by SMA. With the multiyear Landsat TM data, we quantified sub-pixel %ISA and the %ISA changes to assess urban growth in the City of Tampa, Florida during the past twenty years. The experimental results demonstrate that the MESMA approach is effective in mapping and monitoring urban land use/land cover changes using moderate-resolution multispectral imagery at a sub-pixel level.
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