Geography Reference
In-Depth Information
1.3 Research Objectives
The major component in the development of LCLU-maps is satellite imagery. The
objective of this work is the use of high resolution remote sensing data (LAND-
SAT: MSS, TM and ETM+; TERRA: ASTER) for the mapping of land use/land
cover, land use/land cover change, and irrigated agricultural crops. The research
objectives for this study are:
- Understanding the spatial and temporal distribution of the interested study area
surface features;
- Determination of the major dominant LULC in the area using LANDSAT: MSS,
TM and ETM+-, and TERRA: ASTER satellite imagery from 1975, 1987,
2005 and 2007;
- The temporal development mapping of irrigated areas;
- The creation of one classification method to provide a sufficiently accurate
discrimination of the main irrigated crops types in the study area; and
- To determine and analyze the dynamics of change of LULC-classes (trend,
nature, rate, location and magnitude of land use land cover change).
1.4 Research Hypotheses
The rural environments have unique spectral characteristics and the application of
remote sensing provides a unique opportunity to study these requirements. The
process of integration between remote sensing data (in the case studies: LANDSAT
and ASTER), and developments in Computer Science (hardware and software) and
mathematics (algorithms) allows for the mapping of the historical and current land
use and natural land cover, thus ensuring access to the true spatial dimension of
each type of land use. These technologies also allow the study and analysis of the
changes in land use and natural cover over time, and the comparison of the current
status of the region with how it was 30 years ago.
1.5 Organization of the Thesis
This study is organized into seven chapters including this Introductory Chapter,
which provides a statement of intent and sets out research questions, study
objectives and study hypotheses. Chapter 2 covers the necessary basics for
understanding remote sensing in accordance to the current state of the art appli-
cations in use in Syria. Here, the classification process and various classification
algorithms used, including unsupervised and supervised, parametric and
non-parametric, pixel and object classification techniques, are discussed in detail
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