Authors :
Taiwo Qudus; Shuaibu Ade; Mohammed ALIYU Modibbo; Umar Wali; Mustapha Abdulrahman Lawal; Ismail Zahraddeen Yakubu
Volume/Issue :
Volume 8 - 2023, Issue 12 - December
Google Scholar :
http://tinyurl.com/yf338mf9
Scribd :
http://tinyurl.com/yk8sm6zn
DOI :
https://doi.org/10.5281/zenodo.10453284
Abstract :
Federal Government of Nigeria in 1999
embarked on some laudable steps at pioneering the
development of Space Technology in Africa which
eventually led to the launching of Nigeriasat-1 on the 27th
of September 2003 by National Space Research and
Development Agency (NASRDA), Abuja, Nigeria.
However, it is pertinent to know that since the launching
of Nigeriasat-2 satellite, algorithm for the extraction of
feature classes from its image has not been developed.
Therefore, this study is not only exploring the techniques
for the extraction of feature classes from Nigeriasat-2
VHRI but with a view to develop algorithm that will be
more accessible than foreign based algorithms like
eCognition to average Earth Observation Scientists.
Secondly, Desertification is land degradation that occurs
in arid, semi-arid and dry sub-humid areas. Over the
years, desertification and drought are two related
disasters largely contributing to high rate of famine,
especially in the Northern part of Nigeria. This research
has been conducted in order to monitor desertification
extent and severity over northern Nigeria using developed
algorithm from object oriented normalized Bare Surface
index (nBSI). The Global Positioning System (GPS) was
used specifically for picking sample points during ground
sampling and reconnaissance survey. Other image
processing software such as Erdas Imagine, Idrisi
Selva,Ilwis and ENVI were used during various image
processing procedure. Visual basics and Java software
were used for algorithm and final software development.
Six criteria are used to evaluate the performance of a
classification method: accuracy, reproducibility,
robustness, ability to fully utilize the data's information
content, uniform applicability, and objectiveness.. This
method proves valuable to local and regional
governments, educational and research institutions, and
private businesses as a result of its simplicity, low cost,
and integration with a range of software products.
Keywords :
Object Oriented Bare Surface, Feature Extraction Algorithm, Desertification, Early Warning, NigeriaSat-2 and High Resolution Imagery Data.
Federal Government of Nigeria in 1999
embarked on some laudable steps at pioneering the
development of Space Technology in Africa which
eventually led to the launching of Nigeriasat-1 on the 27th
of September 2003 by National Space Research and
Development Agency (NASRDA), Abuja, Nigeria.
However, it is pertinent to know that since the launching
of Nigeriasat-2 satellite, algorithm for the extraction of
feature classes from its image has not been developed.
Therefore, this study is not only exploring the techniques
for the extraction of feature classes from Nigeriasat-2
VHRI but with a view to develop algorithm that will be
more accessible than foreign based algorithms like
eCognition to average Earth Observation Scientists.
Secondly, Desertification is land degradation that occurs
in arid, semi-arid and dry sub-humid areas. Over the
years, desertification and drought are two related
disasters largely contributing to high rate of famine,
especially in the Northern part of Nigeria. This research
has been conducted in order to monitor desertification
extent and severity over northern Nigeria using developed
algorithm from object oriented normalized Bare Surface
index (nBSI). The Global Positioning System (GPS) was
used specifically for picking sample points during ground
sampling and reconnaissance survey. Other image
processing software such as Erdas Imagine, Idrisi
Selva,Ilwis and ENVI were used during various image
processing procedure. Visual basics and Java software
were used for algorithm and final software development.
Six criteria are used to evaluate the performance of a
classification method: accuracy, reproducibility,
robustness, ability to fully utilize the data's information
content, uniform applicability, and objectiveness.. This
method proves valuable to local and regional
governments, educational and research institutions, and
private businesses as a result of its simplicity, low cost,
and integration with a range of software products.
Keywords :
Object Oriented Bare Surface, Feature Extraction Algorithm, Desertification, Early Warning, NigeriaSat-2 and High Resolution Imagery Data.
