Abstract:
Land use and land cover (LULC) changes are one of the main factors contributing
to ecosystem degradation and global climate change. This study used the
Gontougo Region as a study area, which is fast changing in land occupation
and most vulnerable to climate change. The machine learning (ML) method
through Google Earth Engine (GEE) is a widely used technique for the
spatiotemporal evaluation of LULC changes and their effects on land surface
temperature (LST). Using Landsat 8 OLI and TIRS images from 2015 to 2022, we
analyzed vegetation cover using the Normalized Difference Vegetation Index
(NDVI) and computed LST. Their correlation was significant, and the Pearson
correlation (r) was negative for each correlation over the year. The
correspondence of the NDVI and LST reclassifications has also shown that
non-vegetation land corresponds to very high temperatures (34.33°C–45.22°C
in 2015 and 34.26°C–45.81°C in 2022) and that high vegetation land corresponds
to low temperatures (17.33°C–28.77°C in 2015 and 16.53 29.11°C in 2022).
Moreover, using a random forest algorithm (RFA) and Sentinel-2 images for
2015 and 2022, we obtained six LULC classes: bareland and settlement, forest,
waterbody, savannah, annual crops, and perennial crops. The overall accuracy
(OA) of each LULC map was 93.77% and 96.01%, respectively. Similarly, the kappa
was 0.87 in 2015 and 0.92 in 2022. The LULC classes forest and annual crops lost
48.13% and 65.14%, respectively, of their areas for the benefit of perennial crops
from 2015 to 2022. The correlation between LULC and LST showed that the forest
class registered the low mean temperature (28.69°C in 2015 and 28.46°C in 2022),
and the bareland/settlement registered the highest mean temperature (35.18°C in
2015 and 35.41°C in 2022). The results show that high-resolution images can be used for monitoring biophysical parameters in vegetation and surface temperature
and showed benefits for evaluating food security.
Description:
A Publication submitted to the West African Science Service Centre on Climate Change and Adapted Land Use, the Université de Lomé, Togo in partial fulfillment of the requirements for the requirements for the degree of Doctor of Philosophy Degree in Climate Change and Disaster Risk Management
