Abstract:
Remote sensing plays an important role in the continuous observation of the earth's
surface. It produces large databases that can be used to map and monitor natural
resources. To this end, land-use maps are genuine planning and decision-making tools
for natural resource management. The aim of this study is to use remote sensing
products to show the dynamics of land use, to understand these dynamics and their
impact on water resources in general and on hydrology in particular. Specifically, it is
a question of identifying the similarities and differences that have occurred, and finally
evaluating the rate of change. The methodology adopted is the use of the likelihood
algorithm to produce three thematic classifications for the years 1998, 2010 and 2022.
Overall accuracy is determined from the confusion matrix, which ranges from 80% to
91%. Supervised classification yielded seven occupancy classes: water bodies,
cultivated areas, gallery forests, habitats/outcrops, grassy savannahs, wooded
savannahs and bare ground. Quantification of change from 1998 to 2022, assessed by
the rate of change (Tc), shows a progressive trend estimated at 31.82%
(habitats/outcrops) and 43.40% (grassy savannah), and a regressive trend estimated
at -17.92% (cultivated areas); -95.15% (gallery forests); -51.93% (water bodies) and -
78.36% (bare ground). At the end of our study, diachronic analysis enabled us to
quantify changes between the years 1998-2010, 2010-2022 and overall 1998-2022
with good precision.

Keywords: Remote sensing, Land use, Supervised classification, Rate of change, Water resources.