Page 97 - Çevre Şehir ve İklim Dergisi İngilizce - Özel Sayı

Mustafa Sert - Abdullah Emin Akay
Ayhan Ateşoğlu

3. Previous Studies and Operational Methodologies

In order to find the areas with high erosion risk and to take measures
against erosion in the basins where the dams are located, Yüksel et al. (2008)
prepared an erosion risk map by considering the factors (soil, vegetation cover,
topography, climate) affecting the amount of soil erosion in their study ‘Using
the Remote Sensing and GIS Technology for Erosion Risk Mapping of Kartalkaya
Dam Watershed in Kahramanmaras, Turkey’. In their study, they used the
CORINE (Coordination of Information on the Environment) model to produce
erosion risk maps, and ASTER satellite images for land cover classification. In
the CORINE model, slope, vegetation cover/land use capability and erosivity,
which are the necessary database parameters to reveal the erosion status, were
used. The study demonstrated that the use of CORINE model, GIS and remote
sensing technologies produced effective and accurate results in the evaluation
of erosion at low cost and in a short time.
In their study titled ‘Spatial and Temporal Analysis of Turkey Vegetation with
NDVI Images’, Yıldız et al. (2012) analysed the vegetation density of Türkiye and
the temporal change such as the date when the vegetation started and reached
maximum density. The SPOT satellite with a resolution of 1 km and the Normalized
Difference Vegetation Index (NDVI), which is widely used for monitoring vegetation
change, were used in their studies. The Normalized Difference Vegetation Index
is calculated through bands of satellite images obtained with multi-spectral
sensors. The data were analyzed using the VAST software.
NDVIi = (NIR−Red) / (NIR+Red) (1)
In this equation, NIR represents near infrared light with a wavelength of
0.68 - 0.78 μm reflected by vegetation, while Red represents red light with
a wavelength of 0.61 - 0.68 μm absorbed by vegetation (Tucker, 1979). The
results of the study show that the NDVI index can effectively reveal the status
of vegetation cover.
The study by Dindaroğlu and Canbolat (2014), “Determination of Land Use
Classes using CORINE Methodology and Erosion Risk Assessment in Kuzgun
Watershed” examines the effects of land use on erosion. In the study, CORINE
method was used to reveal vegetation classes with Landsat 7 ETM satellite
images. Land use classes were identified with ERDAS Imagine software. The
importance of land use plans in ensuring sustainability was associated with the
capacity of woodlands to minimise the risk of erosion. While it is suggested that
misdirection of land use increases the adverse impacts on natural resources,
the role of vegetation in preventing erosion is emphasised. This study shows
how remote sensing technologies can contribute to the determination of land
use classes, and assessment of drought, desertification and erosion risk.

84 Journal of Environment, Urban and Climate