Identification of urbanization in Ghana based on a discrete approach to analyzing dense Landsat image stacks

Author

Stow, Douglas ; Hsiao-Chien Shih ; Coulter, Lloyd L.

Author_Institution

Dept. of Geogr., San Diego State Univ., San Diego, CA, USA

fYear

2015

fDate

March 30 2015-April 1 2015

Firstpage

1

Lastpage

4

Abstract

In this paper a discrete classification approach to land cover and land use changes LCLUC identification based on stable training sites is tested on a nine-date, four year Landsat-7 ETM+ time sequence for a study area in Ghana that is prone to cloud cover. As an indication of urban expansion, change to Built cover was identified for over 70% of testing units when a spatial-temporal majority filter that ignored No Data values from clouds, cloud shadows and sensor effects was applied. Stable LCLU maps were generated and No Data effects should not limit the potential of the approach for longer-term retrospective analyses or monitoring of LCLUC in cloud prone regions.

Keywords

filtering theory; geophysical image processing; image classification; image sensors; land cover; land use; Ghana; LCLUC map identification; Landsat-7 ETM+ time sequence; cloud shadow; dense Landsat image stack analysis; discrete classification approach; land cover; land use; sensor effect; spatial-temporal majority filter; urbanization Identification; Clouds; Earth; Remote sensing; Satellites; Testing; Training; Vegetation mapping;

fLanguage

English

Publisher

ieee

Conference_Titel

Urban Remote Sensing Event (JURSE), 2015 Joint

Conference_Location

Lausanne

Type

conf

DOI

10.1109/JURSE.2015.7120495

Filename

7120495