Page 102 - Çevre Şehir ve İklim Dergisi İngilizce - Özel Sayı

The use of Geographic Information Systems and Remote Sensing Technologies in
Combating Desertification and Erosion

Alparslan and Küçükönder (2021) conducted a study titled as “Erosion
Susceptibility of the Kaman Sub-Basin” to determine the erosion susceptibility
in the Kaman sub-basin, which is one of the regions where water erosion
is intense. In this study, the Advanced Spaceborne Thermal Emission and
Reflection Radiometer (ASTER), digital elevation model, RUSLE method,
TRMM 3B43 and GloREDa rainfall satellite data were used. ASTER data were
analysed with ArcMap software, which is used to create, edit, analyse and
display geographic data. The Modified Fournier Index (MFI), which addresses
the relationship between sediment transported as a result of erosion, climatic
data and topographic features, was applied.
Modified Fournier Index: It is based on monthly and annual total rainfall
data (Arnoldus, 1977).

(9)
• Pi: Monthly rainfall (mm)
• Pt: Total annual rainfall (mm)
Among the parameters in the RUSLE method:
• Rainfall erosivity factor (R) was obtained through TRMM Multisatellite
Precipitation Analysis, TRMM-PR, VIRS, Tropical Rainfall Measuring
Mission (TRMM) and Microwave Imager (TMI).
• Cover-management factor (C) was calculated via Landsat 8 OLI satellite
images using the NDVI method.
Cr = (− NDVI + 12) (10)
• Cr: Cover-management factor,
NDVI: ((Band 5 – Band 4) / (Band 5 + Band 4)) (11)
The study concludes that the data derived from meteorological satellite
images will contribute considerably when ground-based measurements do
not meet the needs.
In their study ‘Monitoring Desertification Using Machine-Learning Techniques
with Multiple Indicators Derived from MODIS Images in Mu Us Sandy Land,
China’, Feng et al. (2022) conducted a spatial model analysis of desertification
using a combination of multiple indicators and machine learning methods
(Classification Trees and Regression Tree (CART)-Decision Tree (DT), Random
Forest (RF) and Convolutional Neural Networks (CNN)), which are high-precision
and efficient monitoring systems. Moderate Resolution Imaging Spectrometer
(MODIS) satellite data used for climate measurements were combined with
the indices used for desertification monitoring (vegetation index (MOD13A1),
surface reflectance (MCD43A4), land surface temperature (MOD11A2) and
albedo (MCD43A3)). Furthermore, Landsat 8 OLI remote sensing images were
used to visually create and analyse a sample database for machine learning.

89
Special Issue / 2024