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Remote Sensing and GIS for Good Governance: Analysis of High Spatial Resolution Ikonos Imagery for Surveying Agricultural activities in the city of Ouagadougou, Burkina Faso
4. Discussion and conclusions
Conventional spectral classification of IKONOS images proved not to result in reliable maps of
agricultural activities and 'use of waste maps' within Ouagadougou despite the high spatial resolution
of this sensor. Therefore, we developed a new and advanced hybrid approach to include GIS-stored
information in the classification process. This information included distance to irrigation water,
distance to roads and distance to houses. This approach resulted in a set of probability maps of
agricultural activities and use of waste as shown in figure 2. A disadvantage of the new proposed
hybrid classification method is that it does not make optimal use of the information captured by the
IKONOS-2 data. In the procedure only reflectance information is used. A promising approach might
be to include the morphological properties of agricultural land such as shape, size, and patterns in the
classification procedure. Conventional remote sensing methods classify an image on the basis of
spectral data without accounting for spatial patterns. In an urban environment, not only spectral
patterns are important but also spatial patterns are useful to identify certain objects (De Jong et al.,
2000; Van Deursen et al., 1999). To make full use of the information content of an IKONOS image in
an urban environment it might be useful to use classification methods based on spatial patterns, such
as eCognition (Bauer and Steinnocher, 2001).
Other conclusions that came out of this study are that for a cost-effective use of the IKONOS image
there are two important conditions. Firstly, the institute assigned to work with the image should have
access to sufficient hardware and software and sufficient human resources for analysis and fieldwork.
The large size of the IKONOS image file formed a limitation for processing in Ouagadougou.
Analysis and application of such a large data set requires considerable technological resources as well
as human resources. Secondly, the data should be used for as many applications as is permitted by the
license and the results should be made accessible to all potential users. To make data accessible a
system is required to store and order these data, such a system is called a Spatial Data Infrastructure
(SDI). A properly functioning SDI requires long-term investments. Collection of spatial data useable
for environmental research is still a very costly and time-consuming process. A hardware/software
system, organisational structure, human resources and data sets have to be established.
The role that local partners such as INERA can play in the development of an infrastructure for
environmental data for Burkina Faso is large since it is a governmental institute responsible for
collection, analysis and distribution of environmental data. Unfortunately, lack of resources is the
reason many other institutes have invested in data collection and processing. These data are not
regarded as a common resource for the country. This leads to isolated research, duplication of work
and unnecessary costs. Data collected and processed with fiscal or foreign aid resources should be
used to benefit the country. Data and resources like computers should be shared and be available to
the person that needs it most for research. Users and producers of spatial data should form an
information community sharing common digital language and data feature definitions and working
together on problems (Prévost, et al., 1997). The benefit of using an IKONOS image is now mainly
determined by the activities in The Netherlands, since the local institutes lacked human and technical
resources to work with the image. Long-term investments are required to train the local staff and
provide hardware and software systems to work with satellite images. Data and information can then
be used more efficiently and easier be accessed. When these conditions are not assured, the use of
IKONOS to classify (peri)-urban agriculture will not lead to significantly better results than aerial
photography. In fact, the latter will be a cheaper solution and have benefits over the IKONOS image.
Another problem encountered during this study was the dynamic characteristics of agricultural land
use (FAO, 1981). This dynamic character made it very difficult to identify (peri-) urban agriculture
using a single, mono-temporal image. (Peri-) urban agriculture is often not characterised by a certain
spectral or spatial pattern but by the temporal dynamics in both spectral as spatial pattern. In future,
images taken during different moments in the growing season should be combined into one
classification.
Acknowledgements
The research project presented in this paper is financially supported by the INCO programme of the
European Commission under contract no. Contract ERBIC18-CT98-0288 (APUGEDU).
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