In: Wagner W., Székely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
AN ASSESSMENT OF THE EFFICIENCY OF LANDSAT, NIGERIASAT-1 AND SPOT 
IMAGES FOR LANDUSE/LANDCOVER ANALYSES IN EKITI WEST AREA OF 
NIGERIA 
Ojo A G a , Adesina F A b 
a African Regional Centre for Space Science and Technology Education, PMB 019 OAU Campus, Ile-Ife. 
oiobavous@vahoo.com 
b Department of Geography Obafemi Awolowo University, Ile-Ife, Osun State, Nigeria 
faadesin@oauife.edu.ng b 
KEYWORDS: Land-use, Accuracy Assessment, Landsat TM, SPOT XS, NigeriaSat-1, Classification 
ABSTRACT: 
Several remote sensing data types are now available for environmental studies. The variety has increased as many nations 
including some African countries invest in satellite remote sensing. However, each data type has its own peculiar features that may 
limit or enhance its relevance to capture data for specific range of information. This study used geo-information techniques based on 
multi-source imageries to enhance the utilization of images with coarser resolutions in landuse analysis in Ekiti west area of south 
western Nigeria. The objective of the study is to evaluate the variations in landuse characterization with multi-source satellite data 
sets. The remotely sensed data sets used included Landsat TM 1986, SPOT XS 1995 and NigeriaSat-1 2007 satellite images. To 
make the images comparable, they were georeferenced, re-sampled and enhanced for visualization in a GIS environment. The tonal 
values recorded in the images with the features on the ground were validated by ground truthing. The data from ground truthing 
were combined with visual image interpretation for “supervised” classification. The classes defined and analyzed included “built-up 
area”, “bare rock”, “farmland”, secondary forest regrowth” and “water body”. The results show that each image has certain 
relative advantage over the other. For instance, while NigeriaSat-1 image was efficient in the analysis of information within the 
visible portion of the electromagnetic spectrum, SPOT image was better in the Near Infrared. Information from Landsat image was 
rather weak at both portions (Visible and NIR) of the Electromagnetic Spectrum. The study also shows that SPOT image has the 
lowest level of data redundancy of the three image providers. The study confirms the relevance of the growing interest in the use of 
geo-information techniques for landuse analysis. 
1. INTRODUCTION 
Remotely sensed imageries are one of the most important 
sources of spatial data for environmental studies. They are data 
obtained via remotely placed sensors which may be located at 
heights sometime several hundred of kilometres in space to 
make it possible for the sensor to “see” a large portion of the 
earth’s surface at the same time. Such images can also be 
obtained from low flying aircrafts equipped with suitable 
cameras to track earth-based features. These data sets allow 
earth-based phenomena such as landuse and landcover 
characteristics to be rapidly mapped, if needed repetitively and 
at relatively low costs. With increasing capacity to rapidly 
generate maps of large areas, planners in the rural and urban 
areas are getting more empowered to address issues associated 
with landuse analysis such land misuse and various forms of 
incursion into properties and trespassing. 
Some of the most commonly used remote sensing data sets 
for mapping landuse and landcover are those from Landsat, 
SPOT (Système Probatoire d'Observation de la Terre), 1RS 
(Indian Remote Sensing), ASTER (Advanced Spacebome 
Thermal Emission and Reflection Radiometer), MODIS 
(Moderate Resolution Imaging Spectrometer), JERS-1 
(Japanese Earth Resources Satellite), and recently, NigeriaSat- 
1 satellites. The Landsat data have greater spectral resolution 
(Gastellu-Etchegorry, 1990) and a longer time series, while 
SPOT provides better spatial resolution but with shorter 
historical records. Newer satellite imaging systems a” 
commonly equipped with enhanced instruments to generate 
additional data that permit more accurate mapping and analysis. 
Landuse/landcover analyses usually proceed from classification 
of the area of study. The classified units can be further analysed 
in terms of their characteristics particularly size. 
Factors that may influence classification accuracy include 
a sensor’s spatial, radiometry and spectral resolutions. Spatial 
resolution describes the size each pixel represents in the real 
world (Cushnie, 1987). For example, a satellite with 30 metre 
resolution produces pixels that measure a 30x30 metre area on 
the ground. Radiometric resolution, on the other hand, is the 
smallest difference in brightness that a sensor can detect. A 
sensor with high radiometric resolution would therefore have 
very low “noise”. The “noise” is described as any unwanted or 
contaminating signal competing with the desired signal. 
Spectral resolution is the number of different wavelengths that a 
sensor can detect. A sensor that produces a panchromatic image 
alone has a very low spectral resolution, while one that can 
distinguish many shades of each colour has a high spectral 
resolution (Jensen, 2007). 
Generally, spatial resolution is the most important factor of 
the three for landuse and landcover definition. For example 
Gastellu-Etchegorry (1990), in Indonesia studied landuse with 
SPOT and Landsat images. He showed that SPOT Multispectral 
(XS) images are better than Landsat Multispectral Scanner 
(MSS) images for mapping of heterogeneous near-urban 
landcover because of SPOT’s superior spatial resolution. The 
link between spatial resolution and classification accuracy,