الفهرس Only 14 pages are availabe for public view
 |  | from | 224 |  |  |
| | | from | 224 | | |
Abstract
Techniques based on multi-temporal, multi-spectral and satellite-sensor have demonstrated potential as a means to detect, identify, map and monitor ecosystem changes. Multi-temporal images processing becomes more and more important in monitoring earth surface. The large collection of past and present remote sensing imagery makes it possible to analyze spatio-temporal and spectro-temporal pattern of environmental elements. However most existing multi-temporal classification methods use the spectral information alone, ignoring the spatial and temporal correlation between images acquired from different dates, in spite of this represents an amount of information far greater than the individual images. However, their analysis is complex and difficult. This enables to extract evolutions of the same geographic area over time to create a generic spectral signature.
In this thesis, a database of a generic spectral signatures was created for the three main features of the earth; water, vegetation and soil. As large free archives of Landsat 7 ETM+ has been created over time. A temporal series of Landsat 7 ETM+ scenes of different training sites were used to extract the spectral signatures in a reflectance representation by accumulating the individual signatures collected form the individual scenes. The signatures are collected in a statistics form; the mean, minimum, maximum and standard deviations of the training pixels values in the reflectance representation. The database was developed as windows application. Searching has been included to allow users quickly search for the signature of interest. The database was only filled by Landsat 7 ETM+ signatures for the three main features of the earth; water, vegetation and soil which are the features of interest of this study. However, the database is designed to accommodate other generic spectral signatures of different features from other satellites.
T
vi
Temporal series of Landsat 7 ETM+ scenes of five different lakes located in
Egypt are used. The first lake is Burullus Lake at path 177 and raw 38 located in the
Nile Delta. The second lake is Qaroun Lake at path 177 and raw 40 located in Fayoum
governorate. The third lake is Nasser Lake at path 174 and raw 44 located in Aswan
governorate. The selected lakes are typical areas that represent the three main features
of the earth. They typically include water, vegetation and soil. That’s why they are
selected as study areas. All images are converted to reflectance representation form in
order to be independent of the illumination and atmospheric characteristics.
In this thesis, a new spectral classification method is introduced based on a
generic spectral signature stored in a spectral database. The classification process is
done based on the mean, minimum and maximum of the generic spectral signature
from the database. This thesis introduces classification for only the three main classes
of the earth; water, vegetation and soil for only Landsat 7 ETM+. However, it is
supposed that, classification of any other feature for any other satellite can similarly
be done after extracting its corresponding generic spectral signature and store it in the
database. A series of tests was applied on the same used scenes but with different date
for the three main classes; water, vegetation and soil. Accuracy assessment has proven
that the introduced classifier that uses the generic spectral signature gives nearly the
same results of the supervised classification with the parametric decision rule
(minimum distance) that uses the signature that was extracted from each image
individually. It is supposed that any classifier that uses the generic spectral signature
should give better results than using the individual signature extracted from a single
image that is to be classified. Moreover, it is supposed that, the more enhanced
classifiers should give better classification results than the introduced classifier that
based on the minimum distance classifier in its calculations.
A digital change detection approach that based on spectral signature on remotely
sensed data has been proposed. Several examples have been conducted to demonstrate
its performance. The five important inland water sites of Egypt are selected for testing.
The temporal space-borne remote sensing data, Landsat7 ETM+ dated in 2003 and
2014, were used for the implementation and evaluation. This approach can assist to
vii
find the changes spatially, quantitatively and statistically in an automatic way without collecting signatures. This minimizes the manual part of the change detection process.
It has been found a noticeable and clear changes in the land use and land cover of inland water and wetland areas. Investigation of the results of change has proven that; in a time frame of 11 years from 2003 to 2014; there is change by 14.70% (representing 243.93 km2), 4.23% (representing 29.25 km2), 7.66% (representing 35.43 km2), 5.66% (representing 674.02 km2) and 9.70% (representing 49.30 km2) for Burullus, Qaroun, Rayan, Nasser and Morra sites, respectively.
Environmentally, results indicate that the natural resources of the inland water and wetland sites of Egypt are under several pressures and threats from human developmental activities, which may lead to environmental degradation toward their ecosystem tipping points. Therefore, it is recommended that experts of environment, conservation, resource management and sustainability, ecology, biodiversity, eco-system and inland water explain why these changes happened, in order to save these environmental resources. They are also required to develop a plan for sustainable use of these resources to ensure survival of these relevant eco-systems for biodiversity for the next generations. This is an essential step towards an appropriate monitoring and management, since the early warning of these changes is of crucial interest in order to avoid economic or even catastrophic consequences that can result from an accumulation of such changes and may lead to environmental degradation toward their ecosystem tipping points.