Comparison of DMC Nigeriasat-1 SLIM, SPOT-5 HRG and Landsat 7 ETM+ Data for Urban Land Cover Analysis

Type Book
Title Comparison of DMC Nigeriasat-1 SLIM, SPOT-5 HRG and Landsat 7 ETM+ Data for Urban Land Cover Analysis
Publication (Day/Month/Year) 2010
The usefulness of Nigeriasat-1 SLIM data for urban land cover mapping and analysis is
ascertained by comparing it with SPOT-5 HRG and Landsat7 ETM+ data in per-pixel
image classification and object based image analysis (OBIA). This work is part of a larger
study to assess the use of multi-date/multi-sensor satellite remote sensing to monitor and
model urban growth in African cities. Given the long term objective of combining
information from multiple Earth observation satellites and the development and launch of
new Earth observation missions, it is necessary to have a better understanding of their
compatibility. Nigeriasat-1 is one of six satellites, each of which carries a Surrey Linear
Imager sensor (SLIM), that form the Disaster Monitoring Constellation (DMC) (SURREY
SATELLITE TECHNOLOGY LTD 2010). Launched in 2003, it captures data in the green, red
and near infrared (NIR) bands at 32m ground resolution. Earlier attempts to classify
Nigeriasat-1 data for urban land use analysis with single date pixel based classifiers have
not been encouraging due to the limited number of spectral bands and low spatial resolution
(OMOJOLA 2004, OYINLOYE et al. 2004).
Urban development in Africa can be planned or informal. The former is associated with
land use zoning and, in general, more reliable demographic statistics. It follows that it may
be possible to integrate ancillary data with satellite data to extract more useful information
on land cover and land use in these situations (POHL & VAN GENDEREN 1998). Geodata
fusion can be at pixel, feature and decision levels (LEUKERT et al. 2003) and this paper
considers only the first two levels. At pixel level different sensor data are co-registered and
combined for change detection; while at feature level, objects are extracted based on extent,
shape and neighbourhood patterns in an image or from parcel boundaries in the ancillary

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