Introduction - Historical Land Use/Land Cover Classification Using Remote Sensing - page 5

on determining the ability of remotely sensed data to classify, map and update
irrigated land acreage. This was mainly used in the US and India (Draeger 1976;
Rundquist et al. 1989 ). Some new studies have improved and tested new classi-
fication methods particularly appropriated for mapping irrigated lands (Eckhardt
et al. 1990 ; Pax-Lenney et al. 1996; Abuzar et al. 2001 ; Dheeravath et al. 2010 ).
To monitor the changes in our surrounding Earth environment and to manage
the natural resources of the Earth, researchers have presented many models and
strategies, especially during the last few decades. The major element in structuring
these models is how LULC-features change over the time dimension. Land use and
land cover change has become a central component in current strategies for
managing natural resources and monitoring environmental changes. Remote
sensing satellite images have proven their ability in change detection studies.
So-called Change Detection Methods have been applied to multi-temporal images,
in order that variations and changes in the state (especially spectral) and spatial
distribution of features and phenomena can be recognized, mapped and interpreted
(Singh 1989 ; Coppin et al. 2004 ). This method includes procedures, which can
identify and evaluate changes without past or present detailed knowledge of the
land surface (Rogan et al. 2002 ). This information should offer land managers a
better understanding of relations and interactions between the anthropogenic and
natural phenomena. This should be able to offer an efficient distribution and
management of available natural resources. The deep understanding and consid-
eration of all other issues on the reflected EMR-signal, within and between multi
temporal remotely sensed data, will offer the basis for successful change detection
studies (Lu et al. 2004 ).
The research problem is that Syria, in general, and the Euphrates River Basin in
particular, like other developing countries, rely very heavily on traditional statis-
tical methods to monitor and study changes in the natural cover and land use over
time. This is in order to obtain and compare statistical data and figures, and allows
these data to act as a basis and reference to the decision makers in the development
of national plans, including agricultural policy. Based on this collected data, with
regard to the agriculture sector for example, decisions must be made on the
abolition of creative projects, the development of new irrigation schemes, asset-
sufficiency, and whether food should be imported or if local production covers the
needs of the population. Many other procedures and policies rely on the accuracy
of statistical information and data for their success. The statistical methods used in
Syria give unreliable results, because of their complete dependence on the human
element. There is a need for the application of other methods that produce more
accurate data, and which may be less expensive and require less effort. The
inability to represent the distribution and prevalence of various agricultural crops
spatially increases the size of the problem and is a negative factor in statistical data
collection in Syria.
One challenge for researchers lies in the need to find scientific tools and
methods with a suitable methodology, which can be applied to the study area.
Remote sensing can contribute a greater role to the understanding of this problem
by providing accurate mapping data about land uses, including crop utilization